Methods and Systems for Diagnosing, Treating, or Tracking Spinal Disorders

ABSTRACT

A method of patient assessment, treatment, and outcome modeling is disclosed. The method includes obtaining patient characteristic information from a current patient, defining a plurality of therapeutic factors based on the characteristic information of the current patient, and weighting the therapeutic factors. The method also includes accessing at least one database having medical records of prior patients, the medical records including prior patient characteristic information, prior patient treatment plan, and prior patient outcome, comparing the weighted factors of the current patient to the medical records of the prior patients to identify one or more relevant prior patient records, and retrieving at least a portion of the relevant prior patient records, the portion including at least the prior patient treatment plan and the prior patient outcome. The method also includes performing a simulation of at least one of the prior patient treatment plans based on the current patient&#39;s characteristic information and selecting a treatment plan for the current patient.

FIELD/BACKGROUND

The present disclosure is directed to improved systems and methods fordiagnosing, treating, and/or tracking medical conditions. Moreparticularly, in some aspects the present disclosure is directed tosystems and methods for diagnosing, treating, and/or tracking spinaldisorders.

In addition to the areas that are scientific in nature, a significantportion of the practice of medicine is artistic in nature. The medicalprofessional studies at length the biology, physiology and otherdisciplines related to his or her preferred medical specialty orpractice, and thereafter may reference the scientific work of others forassistance. Further, he or she may have specific data obtained frommeasurements or assessments of the particular patient by way of x-rays,thermometers, electrocardiograms, and/or other devices and machines.Even though the data obtained may be undisputed, in many cases the rootproblem may not be entirely clear. Further, even where the root problemis clear there may be several possible treatment options. Accordingly,the physician or other medical professional will rely on experience,skill, and intuition to come to a conclusion as to what the mosteffective treatment may be for the patient.

While decisions based on experience, skill, and intuition are successfulin many instances, in other instances these decisions result in a courseof treatment that is less effective than had been hoped. In such cases,the patient may continue to be subjected to discomfort during theless-effective treatment, or a condition may worsen. Additionally, asmedical procedures and devices become more expensive and time-consuming,it becomes more important to achieve a successful patient outcome in thefirst place from a resource-conservation standpoint as well.Accordingly, there is a need for improved devices, systems, and methodsfor diagnosing, treating, and/or tracking medical problems. For exampleand without limitation, there remains a need for improved devices,systems, and methods for diagnosing, treating, and/or tracking spinaldisorders.

SUMMARY

The present disclosure provides devices, systems, and methods fordiagnosing, treating, and/or tracking medical conditions and, inparticular, spinal disorders.

In certain embodiments, a method of pathology assessment, treatment, andoutcome modeling is provided. The method includes obtaining informationfrom a patient concerning at least one of the patient's characteristics,and defining one or more possible therapeutic outcomes, thereby creatinga plurality of therapeutic factors, and weighting the factors. Accessingat least one database having records of prior treatments of patientshaving similar characteristics, pathologies, and/or therapeutic outcomesand comparing the factors to information in the records. In at least oneembodiment, the most relevant of the records is identified according tothe weighted factors and at least a portion of each of the records isretrieved from the database. In some instances, the portion of therecords obtained includes information regarding an administeredtreatment plan. A simulation and/or outcome modeling of eachadministered treatment from the records obtained is performed to obtaina level of confidence in a particular outcome resulting from saidtreatment. Based on the simulation and/or outcome modeling a treatmentplan for the current patient is selected. The database includesinformation collected from one or more medical treatment studies. Insome instances, the medical treatment studies include general spinaltreatment and outcome studies, spine trauma studies, lumbar spinestudies, cervical spine studies, spinal deformity studies, and/or otherstudies. In some embodiments, the database also includes patientcharacteristic, measurement, and pathology information, includinginformation from diagnostic tests. In some embodiments, some or all ofthe steps of the method are performed electronically, such as over acomputer network. The selected treatment for the patient and its outcomeare provided to a database and/or a medical study in some instances. Insome embodiments, the prior treatments and the administered treatmentsinclude spinal surgical procedures.

In another embodiment, a system for pathology assessment, treatment andoutcome modeling includes a database having a series of records ofpatient treatments, the records including patient measurementinformation, treatment information, and outcome information. In oneaspect, the system also includes at least one processor operativelyconnected to the database and into which a set of information of acurrent patient is entered and weighted. In some instances, theprocessor is programmed to compare the current patient information tothe database information and to output information from records in thedatabase with similar information sets to the current patientinformation. The outputted information includes treatment informationand outcome information. In some embodiments, the at least one processoris programmed to simulate treatment options and/or model outcomes. Theprocessor is programmed for use with item response theory models tocompare said current patient information to the database information insome embodiments. In some instances, the processor is part of a computeror a computer network and in some embodiments includes multipleprocessors at a single or multiple locations.

In another embodiment, a method for pathology assessment, treatment andoutcome modeling includes obtaining a plurality of therapeutic factorsfrom a current patient, including information of at least one of thepatient's characteristics, the patient's pathology, and one or morepossible therapeutic outcomes, weighting the factors, accessing at leastone database having records of prior treatments for patients havingsimilar pathologies, comparing the factors to information in therecords, retrieving from one or more of the records most relevant to theweighted factors, at least a portion of each of the records, theportions including information regarding the outcome of an administeredtreatment, and selecting a treatment for the current patient based atleast in part on said outcome information. The weighting of the factorsvaries in some instances based on the preferences of the practitioner,the hypothesized pathology, experience, and/or other factors. Thetreatment may be performed on the current patient and the database maybe updated with information regarding the patient's treatment andoutcome.

In another embodiment, a method for identifying available treatmentoptions for a patient having an increased likelihood of success isprovided. The method includes obtaining a plurality of therapeuticfactors from a current patient. The factors are based at least partiallyon the current patient's physical characteristics, pathology, anddesired therapeutic outcomes. The method also includes weighting thetherapeutic factors and accessing at least one database having recordsof prior patient treatments. The records including prior patienttherapeutic factors, treatment plans, and treatment outcomes. The methodalso includes comparing the therapeutic factors of the current patientwith the prior patient therapeutic factors in the records of thedatabase to identify prior patients with similar therapeutic factors andretrieving from the database at least a portion of one or more recordsof prior patients with similar therapeutic factors. Finally, the methodincludes identifying available treatment options for the current patientbased at least in part on the records of the prior patients with similartherapeutic factors.

In another embodiment, a system for identifying available treatmentoptions for a current patient having an increased likelihood of successis provided. The system includes at least one local database having aplurality of records of prior local patients. The records of the priorlocal patients includes patient characteristic information, treatmentinformation, and outcome information. The system also includes at leastone remote database having a plurality of records of prior remotepatients. The records of the prior remote patients includes patientcharacteristic information, treatment information, and outcomeinformation. The system also includes at least one processing systemoperatively connected to the local and remote databases. The at leastone processing system includes a diagnostic module, a modeling module,and a treatment module. The diagnostic module is configured to receiveand weight current patient information, compare the current patientinformation to the plurality of records of in the local and remotedatabases, and retrieve records of prior patients with similarcharacteristic information from the local and remote databases. Thetreatment module is configured to identify available treatment optionsfor the current patient based at least partially on the recordsretrieved from the local and remote databases by the diagnostic module.The modeling module is configured to simulate the available treatmentoptions for the current patient identified by the treatment module. Thesimulation is at least partially based on the outcome information fromthe records of prior patients retrieved from the local and remotedatabases.

In another embodiment, a method for identifying available treatmentoptions is provided. The method includes accessing at least one databasehaving records of prior patients. The records include prior patienttreatment plans and treatment outcomes. The method also includesidentifying prior patients with similar characteristics to a currentpatient and retrieving from the database at least a portion of therecords of prior patients with similar characteristics to the currentpatient. The portion of the records retrieved includes the treatmentplans and treatment outcomes of the prior patients with similarcharacteristics. Finally, the method includes identifying successfultreatment plans of prior patients based on the treatment outcomes.

In another embodiment, a method of obtaining and analyzing patientinformation for diagnosis and treatment is provided. The method includesidentifying at least one patient symptom and selecting at least onepatient category associated with the at least one patient symptom. Themethod also includes obtaining data corresponding to the at least onepatient category. The method also provides the obtained data to asoftware application. The software application analyzes the obtaineddata. The method also includes providing a summary of the softwareapplication analysis for use in diagnosing the patient's medicalcondition and identifying available treatment options.

In another embodiment, a method of obtaining and analyzing patientinformation for diagnosis and treatment is provided. The method includessubmitting a patient to diagnostic testing and obtaining results fromthe diagnostic testing. The method also includes categorizing thepatient based on the results from the diagnostic testing. The methodalso includes obtaining additional data regarding the patient. In someinstances, the additional data is associated with the categorization ofthe patient. The method also includes providing the obtained data andthe results from the diagnostic testing to a software application andanalyzing the obtained data and results from the diagnostic testing withthe software application. The method also includes identifying at leastone available treatment option for the patient based on the analysis.

In another embodiment, a method of visualizing and analyzing anatomicalmotion is provided. The method includes providing a plurality ofimplantable sensors. Each of the plurality of implantable sensors isconfigured for implantation adjacent to an anatomical feature of apatient. The method also includes tracking the positions of theimplantable sensors as the patient is put through a diagnostic motionprotocol. The method also includes correlating the positions of theimplantable sensors to the positions of the anatomical features of thepatient adjacent to the sensors. A motion sequence of the anatomicalfeatures is visualized according to the positions of the anatomicalfeatures from the diagnostic motion protocol. Finally, the methodincludes analyzing the motion sequence of the anatomical features toidentify a medical problem.

In another embodiment, a system for visualizing and analyzing anatomicalmotion is provided. The system includes a plurality of implantablesensors. Each of the plurality of implantable sensors is configured forimplantation adjacent to an anatomical feature of a patient. The systemalso includes a monitoring system in communication with the implantablesensors. The monitoring system is configured to track the positions ofthe sensors within the patient during a diagnostic motion protocol. Thesystem also includes at least one processing system in communicationwith the monitoring system. The at least one processing system includesa modeling module configured to create an animated model of thepatient's anatomical features based at least partially on the positionsof the sensors as tracked by the monitoring system during the diagnosticmotion protocol. In some instances, a marker-less or sensor-lesstracking system is utilized. For example, in one embodiment a pluralityof cameras track the patient's motion from different angles. Theresultant images from the cameras are then combined to create 3-Dreconstructions of the motion, which are then mapped to models of thepatient's anatomical features.

In another embodiment, a method of performing a surgical procedure usingimplantable sensors is provided. The method includes providing one ormore implantable sensors. Each of the sensors is configured forimplantation adjacent to an anatomical feature of a patient. The methodalso includes imaging the patient to determine the relative positions ofthe one or more implantable sensors relative to the anatomical featuresof the patient. The method also includes inserting an implant adjacentto at least one of the anatomical features and tracking the position ofthe implant relative to the at least one anatomical feature during theinserting of the implant using the implantable sensors.

In another embodiment, a method of inserting a spinal implant isdisclosed. The method includes providing at least one sensor. The atleast one sensor is positioned within a housing having a bone engagingportion and an asymmetrical head portion. The method also includesengaging the bone engaging portion of the housing with a vertebra. Thepatient is imaged to determine the relative position of the sensorrelative to the vertebra using the asymmetrical head portion of thehousing as a guide. The method also includes inserting an implantadjacent to the vertebra. Finally, the method includes tracking theposition of the implant relative to the vertebra by correlating therelative position of the implant to the sensor to the vertebra.

In another embodiment, a method of selecting implant parameters isprovided. The method includes introducing one or more sensors adjacentto an anatomical feature and monitoring a motion sequence of theanatomical feature with the one or more sensors. The method alsoincludes analyzing the monitored motion sequence of the anatomicalfeature to detect a problem in the motion sequence of the anatomicalfeature. Finally, the method includes determining a parameter for animplant for at least partially correcting the problem in the motionsequence of the anatomical feature.

In another embodiment, a method of selecting a spinal implant and itsparameters is provided. The method includes introducing a plurality ofsensors adjacent to a pair of vertebrae defining a spinal joint andmonitoring a motion sequence of the spinal joint with the plurality ofsensors. The method also includes analyzing the monitored motionsequence of the vertebrae to detect an initial problem in the motionsequence of the spinal joint. The method includes determining aparameter for an implant for correcting the initial problem in themotion sequence of the spinal joint. Finally, the method also includesidentifying at least one spinal implant with the parameter forcorrecting the initial problem in the motion sequence of the spinaljoint.

In another embodiment, a method of detecting implant loosening isprovided. The method includes providing an implant for fixedly engagingwith an anatomical feature of a patient. The implant has a first sensorsecured thereto. The method also includes tracking a first motionpattern of the first sensor and tracking a second motion pattern of asecond sensor secured to the anatomical feature. The method alsoincludes determining a relative motion between the first sensor and thesecond sensor based on the first and second motion patterns. Finally,the method includes identifying implant loosening by analyzing therelative motion between the first sensor and the second sensor.

In another embodiment, a method of detecting implant loosening isprovided. The method includes inserting a first sensor into a bonestructure and securing the first sensor in a fixed position with respectto the bone structure. The method also includes engaging an implant withat least a portion of the bone structure. The implant has a secondsensor positioned therein. The method also includes securing the implantwith the portion of the bone structure such that the second sensor issubstantially fixed with respect to the bone structure and the firstsensor. Finally, the method includes monitoring the position of thesecond sensor with respect to the first sensor to identify implantloosening.

Further aspects, forms, embodiments, objects, features, benefits, andadvantages of the present disclosure shall become apparent from thedetailed drawings and descriptions provided herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagrammatic schematic view of a system for use in treatinga patient according to one embodiment of the present disclosure.

FIG. 2 is a flow chart illustrating a method for diagnosing, treating,and monitoring a patient according to another embodiment of the presentdisclosure.

FIG. 3 is a flow chart illustrating the evaluation step of the method ofFIG. 2 according to one embodiment of the present disclosure.

FIG. 4 is an exemplary screen shot of a software interface that isutilized as part of the evaluation step of the method of FIG. 2according to one embodiment of the present disclosure.

FIG. 5 is a flow chart illustrating the imaging step of the method ofFIG. 2 according to one embodiment of the present disclosure.

FIG. 6 is a flow chart illustrating the patient analysis step of themethod of FIG. 2 according to one embodiment of the present disclosure.

FIG. 7 is a flow chart illustrating the identification step of themethod of FIG. 2 according to one embodiment of the present disclosure.

FIG. 8 is an exemplary screen shot of a software interface that isutilized as part of the identification step of the method of FIG. 2according to one embodiment of the present disclosure.

FIG. 9 is a flow chart illustrating the modeling step of the method ofFIG. 2 according to one embodiment of the present disclosure.

FIG. 10 is an exemplary screen shot of a software interface showing arepresentative figure of the modeling step of the method of FIG. 2according to one embodiment of the present disclosure.

FIG. 11 is a flow chart illustrating the selection step of the method ofFIG. 2 according to one embodiment of the present disclosure.

FIG. 12 is an exemplary screen shot of a software interface that isutilized as part of the selection step of the method of FIG. 2 accordingto one embodiment of the present disclosure.

FIG. 13 is a flow chart illustrating the planning step of the method ofFIG. 2 according to one embodiment of the present disclosure.

FIG. 14 is a flow chart illustrating the performance step of the methodof FIG. 2 according to one embodiment of the present disclosure.

FIG. 15 is a flow chart illustrating the post-treatment analysis step ofthe method of FIG. 2 according to one embodiment of the presentdisclosure.

FIG. 16 is a diagrammatic schematic view of a node for implementing thesystems and methods of the present disclosure according to oneembodiment of the present disclosure.

FIG. 17 is a flow chart of a data flow method according to anotheraspect of the present disclosure.

FIG. 18 is a flow chart illustrating a patient diagnostic modelingmethod according to another aspect of the present disclosure

FIG. 19 is a diagrammatic schematic view of a system for performing themethods disclosed herein according to one aspect of the presentdisclosure

FIG. 20 is a flow chart illustrating a method of collecting andassessing data associated with a method for diagnosing a patient andselecting available treatment options for the patient according toanother embodiment of the present disclosure.

FIG. 21 is a flow chart illustrating a method for diagnosing a patient,identifying available treatment options for the patient, selecting atreatment option for the patient, and performing the selected treatmentoption according to another embodiment of the present disclosure.

FIG. 22 is a diagrammatic schematic view of a data structure for usewith the methods of FIGS. 20 and 21 according to one embodiment of thepresent disclosure.

FIG. 23 is a flow chart illustrating a method for visualizing andanalyzing anatomical motion according to one embodiment of the presentdisclosure.

FIG. 24 is a flow chart illustrating a method for using implantablesensors in an image-guided treatment according to one embodiment of thepresent disclosure.

FIG. 25 is a diagrammatic partial cross-sectional side view of a bonescrew in accordance with one embodiment of the present disclosure.

FIG. 26 is a diagrammatic top view of the bone screw of FIG. 25.

FIG. 27 is a diagrammatic top view of a bone screw similar to that shownin FIG. 26 but illustrating an alternative embodiment of the presentdisclosure.

FIG. 28 is a diagrammatic side view of a system according to anotherembodiment of the present disclosure for using sensors in animage-guided treatment.

FIG. 29 is flow chart illustrating a method for selecting and modifyingimplant parameters using implanted sensors according to one embodimentof the present disclosure

FIG. 30 is a diagrammatic side view of a bone anchor having a pluralityof sensors therein according to one embodiment of the presentdisclosure.

FIG. 31 is a diagrammatic side cross-sectional view of the bone anchorof FIG. 31.

FIG. 32 is a diagrammatic side view of a system for monitoring implantloosening according to one embodiment of the present disclosure.

FIG. 33 is a diagrammatic cross-sectional view of a system formonitoring implant loosening according to another embodiment of thepresent disclosure.

DETAILED DESCRIPTION

For the purposes of promoting an understanding of the principles of thepresent disclosure, reference will now be made to the embodimentsillustrated in the drawings, and specific language will be used todescribe the same. It will nevertheless be understood that no limitationof the scope of the disclosure is intended. Any alterations and furthermodifications in the described devices, instruments, methods, and anyfurther application of the principles of the disclosure as describedherein are contemplated as would normally occur to one skilled in theart to which the disclosure relates. In particular, it is fullycontemplated that the features, components, and/or steps described withrespect to one embodiment may be combined with the features, components,and/or steps described with respect to other embodiments of the presentdisclosure.

Referring to FIG. 1, shown therein is a diagrammatic schematic view of asystem 10 for use in treating a patient according to a first embodimentof the present disclosure. Among other aspects, the system 10 is used bymedical professionals and other medical personnel for assisting them inmaking decisions on treatments aimed at the desired clinical outcomesfor the patient. The system 10 includes methods for coordinating anumber of sources of data and assimilating the relevant data to provideactionable information for the medical personnel. Using such informationwith the appropriate confidence intervals, the medical personnel developa clinical decision on an appropriate treatment plan based on data ofactual patient outcomes relating to identical or similar treatments. Inparticular embodiments, the data analysis takes the form of an itemresponse theory (IRT) modelization technique that allows medicalpersonnel to compare the patient's symptoms to previous patients havingsimilar symptoms, and then identify the treatments and correspondingoutcomes for the previously treated patients. The previous patientoutcomes are conditioned or weighted based on a probabilistic treatmentresult. Based on the available data, the system 10 objectively decidesone or more of the variable or “trade-off” decisions typically performedby skilled professionals that are used to select among a plurality ofdifferent treatment options.

In some instances, the system 10 is continuously optimized by trackingpatient outcomes with the corresponding treatment plans and modifyingtreatment plans for future patients accordingly. In that regard, in someinstances the system 10 utilizes fuzzy logic and/or genetic algorithmsfor correlating future patient symptoms with prior patient symptoms andoutcomes to identify the best treatment plan for the patient based ondesired outcomes. In that regard, patient outcome goals may include,without limitation, a particular Oswestry disability form and score(ODI), overall patient satisfaction with treatment, reduction orelimination of symptoms (e.g., pain, limited mobility, etc.), improvedscore on Neck Disability Index (NDI), improved SF-36 (Short Form 36)score, improved HRQL (Health Related Quality of Life) score, restorationor improvement of quality of life, and/or other factors. Moreparticularly, where surgical treatments are undertaken the outcome goalsmay include, without limitation, restoration or improvement of mobility,restoration or improvement of range of motion, restoration orimprovement of balance in the sagittal and/or coronal planes,restoration or improvement of center of gravity, preservation of healthyanatomy (ligaments, cartilage, bony anatomy, etc.), restoration orimprovement of gait, and/or other factors. In addition to the patientspecific surgical outcome goals, the present disclosure provides methodsand systems for improving aspects of surgical procedures in generalincluding, without limitation, improved cosmesis (particularly muscularand skin incision in some instances), reduced harvest morbidity, reducedneed for harvests (BMP), reduced morbidity, reduced complications,reduced cost of surgery, reduced time in operating room, reducedrecovery time, reduced blood loss, reduced likelihood of revisionsurgery, reduced likelihood of adjacent level disc disease, reducedadjacent organ restrictions or impairment (e.g., lung) caused by traumaor extreme deformity, and/or other factors.

While the systems and methods disclosed herein will be described in thecontext of orthopedic treatments and, in particular, with surgicalorthopedic treatments of the spine, it is understood that systems and/ormethods similar to those described herein are useful for diagnosing andtreating patients in numerous other medical fields, including but notlimited to cardiology and oncology. Further, while the treatment plansdiscussed herein will be focused on surgical procedures, the treatmentplans also include non-invasive treatments, such as physical therapyprotocols, in addition to or in lieu of the surgical procedures in someembodiments. Similarly, the treatment plans also include medicinaltreatments as well in some embodiments.

Referring again to FIG. 1, the system 10 includes a diagnosis module 12,a modeling module 14, a treatment module 16, and a post-treatmentfeedback module 18. In some embodiments, the system 10 is a spinaldisorder diagnostic and treatment system. In that regard, the system 10is utilized by physicians, surgeons, medical assistants, and/or othermedical personnel to diagnose motion disorders, model patient-specifictreatment plans, plan and deliver treatment to the patient, acquirefeedback regarding the effectiveness of the treatment, modify thetreatment as needed, track patient results based on treatment plans,and/or continuously improve patient treatment by correlating successfultreatment plans with specific patient symptoms or characteristicsrelated to the motion disorders. As described below, the system 10provides communication between medical personnel (physicians, surgeons,therapists, medical assistants, etc.), patients, and/or medical devicemanufacturers. Also, the system 10 is particularly adapted for providinga platform for executing the methods described below. Additional detailsregarding the modules 12, 14, 16, and 18 of the system 10 will bedescribed in more detail below in relation to these methods. Other usesof the system 10 and its modules 12, 14, 16, and 18 will be apparent toone skilled in the art from the following description and should beconsidered part of the present disclosure.

Referring to FIG. 2, shown therein is a flow chart illustrating a method20 for diagnosing, treating, and monitoring a patient according toanother aspect of the present disclosure. The method 20 begins at step22 with evaluation of the patient. The evaluation determines whether thepatient should be subjected to the subsequent steps of the method 20. Inthat regard, the evaluation may vary depending on the types oftreatments contemplated by the method 20. For example, in someembodiments the method 20 is based predominantly on surgical treatments.In such embodiments, the evaluation at step 22 focuses on determiningwhether the patient is a potential candidate for the available surgicaltreatments. If the evaluation at step 22 indicates that the patient isnot a candidate for the available surgical treatments (e.g., due to ageor other factors), then the subsequent steps of the method 20 are notperformed. On the other hand, if the patient has a condition or symptomthat indicates that the patient is definitely a candidate for theavailable surgical treatments (e.g., spondylolisthesis of grade 2 ormore), then the evaluation step may either be truncated or completelyskipped, and the method 20 may continue with the subsequent steps.

Referring more specifically to FIG. 3, shown therein is a flow chartillustrating the evaluation step 22 of the method 20 according to oneembodiment of the present disclosure. Generally, the evaluation at step22 will focus on determining whether the patient is a candidate for thecontemplated treatment options. For example, in the context of spinaldisorders the method 20 provides treatment options that include spinalimplants and related surgical techniques for correcting the spinaldisorder. The evaluation step 22 includes obtaining information from thepatient. Such information may include standard information on thephysical characteristics and condition of the patient. In a particularembodiment, such information includes a series of options for thepatient and/or the medical professional to select. Such options aregeared toward helping to determine an appropriate treatment, asdisclosed herein, and also include goals as to post-operative mobility,activity, or relative deformity; pre-operative condition; priorsurgeries or other treatments; or other factors. In the currentembodiment, at least some information is obtained by determining theanswers to a series of diagnostic questions in step 78. The diagnosticquestions include questions such as: How far can the patient walkwithout pain? Does the patient have pain lying down? Does the patienthave pain sitting? Does the patient have pain standing? Does the patienthave back pain with leg pain? If yes, is the leg pain localized orradiating? These are exemplary questions and are not to be consideredlimiting. Numerous other questions may be utilized to evaluate thepatient. In that regard, the questions are nested such that subsequentquestions depend on the answers to previous questions. Further, some orall of the information provided is weighted so as to emphasize one ormore factors as the analysis of potential treatments is performed insome instances. That is, answers to particular questions or informationprovided are given more importance than other answers or information.

In some embodiments, the questions of step 78 are provided to thepatient and/or medical personnel in an interactive computer program. Insome embodiments, the diagnosis module 12 of the system 10 (FIG. 1)prompts a user to answer the series of diagnostic questions and/orprovide menus for selecting items indicative of the patient's medicalcondition. Referring to FIG. 4, shown therein is an exemplary screenshot 40 of a software interface that is utilized as part of theevaluation at step 22. A spinal disorder selection menu 42 is providedon the left hand side of the screen shot 40. The menu 42 provides a dropdown menu containing a plurality of spinal disorders. The treatingphysician or other medical personnel selects the spinal disorder(s)afflicting the patient using the menu 42. In other embodiments, the menu42 includes symptoms (e.g., low back pain, limited flexion, etc.)instead of or in addition to the spinal disorders in some embodiments. Anote field 44 is provided on the right hand side of the screen shot 40allowing additional information regarding the patient to be recorded. Itis understood that the screen shot 40 represents a single part of theevaluation step 22 and is not to be considered limiting. In that regard,the evaluation step 22 includes one or more additional pages or screenshots containing additional questions, menus, and/or other inputsrelated to the patient's condition in some embodiments.

Referring again to FIG. 3, in addition to or in lieu of the diagnosticquestions 78 the evaluation at step 22 includes other types of patientanalysis. In the current embodiment, imaging techniques are utilized toevaluate the patient at step 80. For example, in some embodimentsradiographic images of the patient's anatomy are obtained. Theradiographic images are then analyzed to identify any medical conditionsafflicting the patient. The medical conditions are then considered as afactor in evaluating the patient. In some embodiments, the patient isput through a series of movements appropriate to determine the patient'smotion sequence and/or range of motion for one or more anatomical areas.The patient's motion sequence and/or range of motion in each area isthen considered as a factor in evaluating the patient. Additionalconsiderations and/or tests are taken into account during the evaluationof the patient as desired by the treating physician or other medicalpersonnel.

Based on the response to the diagnostic questions 78, imaging data 80,and/or other types of patient analysis, the patient can be grouped intoa classification at step 82. In some embodiments, the classification 82is by type of injury or medical condition. In other embodiments, theclassification 82 is based on other patient factors. In someembodiments, the classification 82 is at least partially based on atreating physician or other medical personnel's preferences. It iscontemplated that, in some embodiments, each classification is furthersubdivided into groups based on factors such as the severity of thecondition, age, health, and/or other factors. In some embodiments, theclassifications and groupings are based on factors identified inclinical studies and/or past patient treatments as being indicators ofsuccess for the available treatment options. A general determination canbe made regarding whether the patient is a candidate for the availabletreatment options based on the grouping and classifications. In thatregard, it is contemplated that each classification or grouping definesan inclusion group that indicates that the patient is a candidate for anavailable group of treatment options. If the patient is not a candidatefor the available treatment options then the method 20 terminates. If,however, it is determined that the patient is a candidate for theavailable treatment options, then the method 20 continues with step 24.

At step 24, the patient is subjected to an imaging study. Referring morespecifically to FIG. 5, shown therein is a flow chart illustrating theimaging step 24 of the method 20 according to one embodiment of thepresent disclosure. The imaging study includes obtaining patient imagesthrough the use of magnetic resonance imaging (“MRI”), computedtomography (“CT”), video fluoroscopy, and/or other imaging techniques atstep 84. In some embodiments, the imaging study includes techniques asdescribed in commonly owned U.S. patent application Ser. No. 11/697,426filed Apr. 6, 2007 and titled “System and Method for Patient Balance andPosition Analysis”, herein incorporated by reference in its entirety. Ingeneral, the imaging study obtains images of the patient's anatomy thatare utilized in subsequent steps of the method 20. In particular, theimaging study of step 24 focuses on obtaining images and/or informationnecessary to model portions of the patient's anatomy.

In some embodiments, the imaging study of step 24 includes tracking themovement of anatomical features of the patient using sensors. In someembodiments the sensors are implantable and are placed in direct contactwith and/or within the relevant anatomical feature(s) of the patient. Inother embodiments, the sensors remain outside of the patient's body, butare positioned in close proximity to the anatomical feature(s) ofinterest. For example, in some embodiments the imaging study tracks theposition of at least some of a patient's vertebrae. In one embodiment, asensor is implanted into each vertebra and the location of the vertebrais tracked using the sensor. In another embodiment, a sensor is placedoutside the patient's body adjacent the spinous process. The location ofthe spinous process and, in turn, the vertebra are tracked using thesensor. In some embodiments, the position of the sensors and anatomicalfeatures are tracked while the patient is put through a particularmotion sequence or protocol. For example, in one embodiment the patientis asked to walk on a treadmill. The position of the sensors andanatomical features are tracked and correlated to the patient's gaitcycle. It is understood that these described uses of sensors are merelyexemplary and should not be considered limiting. Sensors, implantable orotherwise, may be utilized in numerous other combinations and ways totrack the position of anatomical features during the imaging study.

The data from the imaging study is provided to one or more softwareapplications at step 86 in order to derive further information and/ornew views of the imaging data. Generally suitable software packages willbe capable of one or more of 2-D radiographic measurement and analysis;3-D modeling, reconstruction, and kinematic simulation; therapy modelingor simulation; and outcome simulation. Examples of such software includethe Montreal 3D Radiographic Modeling, Measurement and SurgerySimulation software (“Montreal software”); the TruBalance patientmeasurement software (“TruBalance software”); and the DRPro radiographicmeasurement software offered by PhDx eSystems, Inc. of Albuquerque.Other brands or types of software for obtaining, analyzing, or otherwisehandling patient data may be used in addition to or instead of one ormore of the software applications mentioned above for one or more of thedata categories. Also, multiple software applications may be applied toa given set of data. It is understood that data from each study can beassembled together prior to submission to such software, or each studycan be treated individually.

In some embodiments, the Montreal software is used to generate athree-dimensional model of the patient's spine, the TruBalance softwareis used to calculate a global balance for the patient, and the DRProsoftware is used to measure the images. In some embodiments, anadditional step that can be used is to measure the images with softwareknown as Clindexia. At step 88, these software applications transformthe raw images into mathematical or other forms that can be manipulatedvia a computer system and compared to other images and/or other datasets.

Referring to FIGS. 2 and 6, after the imaging study of step 24, themethod 20 continues with step 26 in which a patient analysis isperformed. Referring more particularly to FIG. 6, shown therein is aflow chart illustrating the patient analysis step 26 of the method 20according to one embodiment of the present disclosure. Generally, thepatient analysis of step 26 synthesizes the information obtained duringsteps 22 and 24 to identify the abnormal medical conditions afflictingthe patient. In that regard, in the current embodiment step 26 beginswith retrieving the patient evaluation and/or imaging study data fromsteps 22 and 24 at step 90. The patient analysis step 26 continues withstep 92 in which a 3-D and/or 2-D animated model of the patient'sanatomy is created. Generally, the animated model is based on the dataobtained from the imaging study of step 24. In some embodiments, theanimated model is used to highlight the problem areas and/or times inthe patient's anatomical motion sequence or motion pattern. In thatregard, motion sequences and/or motion patterns as the terms are usedherein are intended to include a patient's gait, a portion of thepatient's gait, a single movement of a single anatomical structure, aseries of movements of a single anatomical structure, a single movementof a plurality of anatomical structures, a series of movements of aplurality of anatomical structures, or other aspects of a patient'smotion. Generally, any patient motion in whole or part may be referredto as a motion sequence or motion pattern.

The model of the patient's anatomy includes layers of anatomicalfeatures that are selectively included or removed. For example, in oneembodiment the patient's motion anatomy is grouped into layers accordingto types of anatomical tissue, such as bones, cartilage, ligaments,tendons, muscles, and/or combinations thereof. The animated model thenanalyzes motion according to each grouping of anatomical tissue and theinteractions therebetween.

In some embodiments, the animated model combines diagnostic tests withthe imaging study. For example, in some embodiments the animated modelcombines muscle monitoring with the imaging study to identify musclecontractions and tensions during a motion sequence or protocol. Theresults of the muscle monitoring are combined with the other imagingdata to provide additional details and/or realism to the animated model.In other embodiments, the animated model utilizes center-of-balance orcenter-of-gravity data for the patient obtained during the motionsequence or protocol. Muscle monitoring and center-of-balance data aremerely examples of the types of additional data that may be combinedwith the imaging data in forming the animated model. Other types of thepatient data may also be utilized. In that regard, in some embodimentsthe treating physician or medical personnel selects the types of patientdata to be used in formulating the animated model.

The animated model includes additional features to allow medicalpersonnel and/or a computer system to analyze the patient. In thatregard, in some embodiments the animated model includes a stress gridoverlay that indicates potential areas of increased stress or strain onthe patient's anatomy, such as increased muscle activity; overstretchingof muscles, ligaments, and/or tendons; friction between bones; and/orother areas of stress/strain. In some embodiments the model allows forzooming, panning, or otherwise changing the orientation of the view ofthe patient's anatomy. A user adjusts the orientation to better observeor isolate a potential problem area. Similarly, the animated modelallows a user to pause, rewind, slow down, and/or speed up simulation ofa motion sequence to better observe a potential problem. Further, theanimated model allows 3-D and/or 2D tracking of specific anatomicalfeatures through the motion sequences. At step 94, the animated modelhighlights potential problem areas automatically based on a comparisonto a standardized model associated with the patient and/or the treatingphysician or medical personnel highlights potential problem areas basedon their observations. In some embodiments, the problem areas areidentified by a computer system and/or medical personnel by recognizingan abnormal motion pattern(s).

At step 96, a statistical summary of the patient analysis is provided.The summary provides information important to the diagnosis andsubsequent treatment of the patient's medical condition. In someembodiments, the statistical summary identifies such things as damagedanatomical features or areas, limited ranges of motion, and/or otherdata related to the patient's condition. The information provided is atleast partially determined by the medical personnel. For example, insome embodiments the medical personnel selects or otherwise accesses theparticular information or data sets they deem to be most important indiagnosing and treating the patient. In some embodiments, thestatistical summary provides a comparison to other patients with similarmedical conditions, medical histories, and/or patient profiles. Further,the selected treatment plans and relative success of those plans for theother patients is provided. The statistical summary also provides a listof possible causes for the medical condition and/or identifies possiblerelationships between abnormal motion patterns.

Referring to FIGS. 2, 7, and 8, after the patient analysis of step 26,the method 20 continues with step 28 in which the available treatmentoptions are identified. The treatment options are based upon the patientanalysis. Referring more particularly to FIG. 7, shown therein is a flowchart illustrating the identification step 28 of the method 20 accordingto one embodiment of the present disclosure. In this particularembodiment, the treatment options are determined by looking at thestatistical summary of the patient analysis at step 98, identifying thepatient's medical condition(s) at step 100, and proposing treatmentplans based on the patient's medical condition(s) at step 102. Theproposed treatment plans include surgical procedures, non-invasivetreatments, and/or medicinal treatments. For sake of example andsimplicity, a series of proposed surgical treatment plans will now bediscussed in the context of a disc herniation in the lumbar region ofthe spine as identified by a patient analysis. This is for exemplarypurposes only and should not be considered limiting in any way.

Referring more particularly to FIG. 8, shown therein is an exemplaryscreen shot 46 of a software interface that is utilized as part ofidentifying the available treatment options at step 28. In the currentembodiment, a spinal disorder menu 48 is provided on the left hand sideof the screen shot 46. The spinal disorder menu 48 currently indicatesthat the patient suffers from a lumbar disc herniation. In otherinstances, the patient may suffer from other spinal disorders and/or aplurality of spinal disorders. A treatment menu 50 is provided on theright hand side of the screen shot 46 and includes a plurality oftreatment plans. The treating physician or other medical personnelselects one or more of the treatment plans from among the plurality oftreatment plans using the treatment menu 50. In the current embodiment,the treatment menu 50 provides a plurality of surgical treatment optionsfor correcting a lumbar disc herniation. In some embodiments, thetreatment menu 50 includes an option allowing a surgeon or otherphysician to input a treatment plan based on her own experience that isnot included in the plurality of treatment options.

Referring again to FIGS. 2, 7, and 8, in some embodiments the treatmentoptions of step 28 are sorted and/or screened based on physicianpreference at step 104. For example, if a physician prefers surgicalprocedures that utilize a posterior approach, then the availabletreatment options are limited to those implants and surgical proceduresthat are implanted through a posterior approach. As another example, thetreatment options are sorted based on the success of the treatment planfor previous patients having a similar profile to the current patient.Similarly, in some embodiments the treatment options are sorted based onthe previous procedures performed by the treating physician/surgeon andthe relative success of those procedures. In other embodiments, thepatient suffers from medical conditions unrelated to the spine that ispresented in a similar manner—indicating the medical condition andproposing a plurality of treatment options.

Referring to FIGS. 2, 9, and 10, after identifying one or more of theavailable treatment plans at step 28, the method 20 continues at step 30with modeling of the available treatment options. Referring morespecifically to FIG. 9, shown therein is a flow chart illustrating themodeling step 30 of the method 20 according to one embodiment of thepresent disclosure. Modeling of the treatment options builds upon theanimated model of the patient analysis of step 26. In that regard, themodeling step 30 begins with retrieving the model of the patient'sanatomy at step 106. Next, the modeling step 30 continues by modifyingthe 3-D and/or 2-D animated model of the patient's anatomy according tothe treatment plan at step 108. For example, in some embodiments theanimated model is modified by replacing a damaged portion of thepatient's anatomy with an implant. A model can then be created utilizingthe characteristics of the implant in place of the damaged portion ofthe patient's anatomy as indicated by step 108. Referring to FIG. 10,shown therein is a screen shot 52 of a software interface showing arepresentative figure of a modeling according the present embodiment.

In some embodiments, the modeling is used to identify potential problemareas and/or times in the patient's anatomical motion sequence thatremain after implantation of the implant at step 110. In that regard, aspreviously described the model includes layers of anatomical featuresthat may be selectively included or removed, such as bones, cartilage,ligaments, tendons, muscles, and/or combinations thereof. The modelanalyzes the motion sequence at each level of anatomical tissue with theimplant in place and then the model the resultant motion sequenceincluding all of the levels. In that regard, in some embodiments themodel takes into account the surgical procedure or approach utilized ininserting the implant. For example, if muscles, tendons, cartilage,and/or other supporting tissues will be cut or resected during thesurgical procedure, then the model takes this into account in modelingthe resultant motion sequences. The model highlights potential problemareas automatically based on a comparison to a standardized modelassociated with the patient and/or the treating physician or medicalpersonnel may highlight potential problem areas based on theirobservations of the resultant motion sequence. In some embodiments, theproblem areas are identified by a computer system and/or medicalpersonnel by recognizing or tracking an abnormal motion pattern(s).

By identifying potential problem areas and/or times in the patient'sanatomical motion sequence and taking into account the tissues that willbe compromised during the surgical procedure, the modeling provides arealistic estimation of the resultant outcome of the treatment plan. Inthat regard, the treating physician optimizes each treatment plan bymodifying such factors as the size, placement, orientation, and materialproperties of a particular implant and/or modifying the surgicalprocedure to adjust the tissues that will be compromised at step 112.Further, the treatment plan is modified according to weighted factorsconcerning the patient's characteristics and/or the desired outcome atstep 112. After the treatment plan is modified the modeling step 30 mayreturn to step 108 and update the model according to the modifiedtreatment plan. This process may be iterated until the physician issatisfied with the parameters of the treatment plan. For each of theselected treatment plans and/or implants, the treating physician savesone or more optimized plans in a database or other accessible memorylocation. A statistical summary of the optimized treatment plan isprovided for each selected treatment plan at step 114.

Additional features as previously mentioned may be utilized to model thetreatment plans. In some embodiments the model includes a stress gridoverlay that indicates potential areas of increased stress or strain onthe patient's anatomy, such increased muscle activity; overstretching ofmuscles, ligaments, and/or tendons; friction between bones; and/or otherareas of stress/strain caused by the implant and/or treatment plan. Insome embodiments the model allows for zooming, panning, or otherwisechanging the orientation of the view of the patient's anatomy with theimplant inserted. A user adjusts the orientation to better observeplacement and/or functioning of the implant. Similarly, the model allowsa user to pause, rewind, slow down, and/or speed up simulation of amotion sequence to better observe the patient's motion with the implant.Further, the model allows 3-D and/or 2D tracking of specific anatomicalfeatures through the motion sequences.

In some embodiments, the method 20 does not include step 30. In otherembodiments, the method 20 includes the modeling and optimization ofstep 30 with respect to only some of the selected treatment plans. Inthat regard, some treatment plans do not lend themselves to modelingand, therefore, may not be modeled even when other selected treatmentplans are modeled. Further, the example described above focused ontreatment plans including insertion of implant and the correspondingsurgical procedures. It is understood that similar modeling andoptimization approaches are utilized to model non-surgical proceduresand/or other types of treatment plans in some embodiments.

Referring to FIGS. 2, 11, and 12, after optimizing each of the selectedtreatment plans at step 30, the method 20 continues at step 32 where atreatment option is selected. Referring more particularly to FIG. 11,shown therein is a flow chart illustrating the treatment plan selectionstep 32 of the method 20 according to one embodiment of the presentdisclosure. Generally, a physician and/or a computer system compares themodeled results and/or statistical summaries for each of the optimizedplans and selects the plan best suited for correcting the patient'smedical condition. The selection step 32 begins with retrieving thestatistical summaries of the available treatment options at step 116.The plan best suited for the patient is based on such factors as thepatient's profile, the desired results, the physician's preferences,and/or the patient's preferences. It is contemplated that in someinstances a computer system ranks the treatment options based on theresults of previous patients having similar profiles to the currentpatient. In that regard, the computer system includes the confidencelevel for particular outcomes for each treatment option in someembodiments. Accordingly, at step 118 the statistical summaries of theavailable treatment options are compared to the desired patientoutcomes. With the clinical outcomes modeled and the results displayedwith respective confidence intervals or levels for each outcome relatedto the particular treatment options, medical personnel can make theappropriate decisions for treating the patient with by balancing thetrade-off of parameters that important to the medical personnel and thepatient's outcome. The medical personnel's decision can be made fromactual patient data relative to a similar condition that representstheir particular patient's problem. In the end, taking all of thevarious considerations into account the best available treatment optionfor the patient is selected at step 120.

In addition to selecting a treatment option, step 32 also includesdiscussing the selected treatment option with the patient at step 122.In that regard, the results of the analyses and modeling are shownand/or explained to the patient to support the decision to go with aparticular treatment. Further, in the case of a treatment plan thatincludes inserting an implant or otherwise employing a medical device,the patient may be given access to additional product informationregarding the medical device. In some embodiments, discussing thetreatment option with the patient is accomplished over the internet, anintranet, computer network, telecommunications network, or other type ofremote connection. In that regard, the link between the patient and themedical professional may be a secure link or secured communicationchannel so as to protect the patient's confidentiality. In someinstances, the treatment options are provided over a secure website. Thepatient is provided access to the secure website via a username andpassword associated with the patient. In addition to providing thepatient information regarding the selected treatment option(s), thepatient interface also provides the patient with the ability to askquestions. In some embodiments, the interface includes a query box thatis filled out and submitted by the patient, which a medical professionalreplies to. In other embodiments, the interface is in the form of a chator instant messaging session. The patient may ask questions over thechat session and the medical personnel can provide answers to thesequestions immediately or seek answers to the questions and reply to thepatient at a later time. In yet other embodiments, the patient interfacemay be combined with video-conferencing or telephonic-conferencing toprovide additional information and opportunities for questions to thepatient.

Referring to FIG. 12, shown therein is an exemplary screen shot 54 of asoftware interface that may be utilized as part of step 32. In thecurrent embodiment, a link 56 to a product information page regarding animplant designed for the patient is provided on the left hand side ofthe screen shot 54. In that regard, the product information pagedesigned for the patient includes generalized information regarding thetype of implant, the typical uses of the implant, specifications of theimplant, success stories related to the implant, and/or otherinformation related to the implant that would be desirable to share witha patient. A link 58 to a product information page regarding an implantdesigned for the physician or medical personnel is provided on the righthand side of the screen shot 54. The product information page designedfor the physician includes information related to the appropriatesurgical approaches available for inserting the implant, details of thepreferred surgical procedure(s), specifications of the implant,available variations/models of the implant (e.g., sizes, materials,etc.), and/or other information related to the implant that would bedesirable to share with the physician.

Referring to FIGS. 2 and 13, after selection of the treatment option atstep 32, the method 20 continues with step 34 in which the execution ofthe selected treatment option is planned. In this regard, a majorityand/or all of step 34 is included in the optimization of the treatmentplans in step 30. However, not all of the details of executing thetreatment plan are necessarily addressed in step 30. Further and asnoted previously, in some embodiments step 30 is not included and,therefore, planning the execution of the treatment option is completedin step 34. In some embodiments, planning the treatment option comprisesplanning the surgical procedure utilized to insert the implant. Forexample, referring more particularly to FIG. 13, shown therein is a flowchart illustrating the planning step 34 of the method 20 according toone embodiment of the present disclosure where the selected treatmentoption is a surgical procedure. The planning step 34 begins withdetermining the desired placement and orientation of the implant at step124. The planning step 34 continues by identifying any anatomicalfeatures that need to be preserved through the surgical procedure atstep 126. Further, the desired fixation positions and orientations forany fixation devices are established and marked on a model at step 128.These fixation positions and orientations are saved for future referenceduring the actual surgical procedure. With respect to the plannedplacement and orientation of the implant and/or fixation devices, anerror field is established that identifies the expected range ofaccuracy within which the implant and/or fixation devices should beimplanted. Based on this expected range of accuracy, a correspondingexpected range of performances is established for the treatment plan.Image guided surgery techniques are utilized in some embodiments toensure that the treatment plan is executed according to the desiredpositions and orientations. Further, the position of the patient duringeach step of the treatment plan is determined in some instances. Forsome treatment plans the patient is moved between different positionsfor various steps of the treatment plan. Accordingly, in someembodiments the selected treatment option is planned in accordance withuse of a dynamic surgical table as described in U.S. Pat. No. 7,234,180,filed Dec. 10, 2004 and titled “Dynamic Surgical Table System,” herebyincorporated by reference in its entirety. Taking these various factorsinto consideration planning the execution of the selected treatmentoption is finalized at step 130.

Referring to FIGS. 2 and 14, after planning the execution of thetreatment plan at step 34, the method 20 continues with step 36 in whichthe treatment plan is executed. Referring more particularly to FIG. 14,shown therein is a flow chart illustrating the performance step 36 ofthe method 20 according to one embodiment of the present disclosure. Atstep 132, the treatment plan is executed in accordance with the planningthat has occurred in the previous steps. In that regard, in the contextof a surgical procedure the procedure is monitored intra-operatively atstep 134. The actual surgical procedure, as monitored, is compared inreal-time, or approximately real-time, to the planned treatment at step136. Thus, the actual placement of the implant and fixation devices iscompared to the intended placement and/or associated error fields. Inthis manner, an analysis of the placement of the surgical components isperformed before the patient leaves the operating room. At step 138, theactual surgical procedure is modified as needed to ensure that itcoincides with the error fields of the planned treatment. Thus, anyinitial adjustments that need to be made can be accomplished without theneed for a revision surgery or a return to the operating room. In someembodiments, the position of the implant and/or fixation devices isestablished using implantable sensors located within the implant,fixation devices, and/or insertion instruments. For example, in someinstances the implant and/or fixation devices include sensors such asthose described in U.S. patent application Ser. No. 10/985,108 filedNov. 10, 2004; U.S. patent application Ser. No. 11/118,170 filed Apr.29, 2005; U.S. patent application Ser. No. 11/344,667 filed Feb. 1,2006; U.S. patent application Ser. No. 11/344,999 filed Feb. 1, 2006;U.S. patent application Ser. No. 11/356,687 filed Feb. 17, 2006; U.S.patent application Ser. No. 11/344,459 filed Jan. 31, 2006; U.S. patentapplication Ser. No. 11/344,668 filed Feb. 1, 2006; each of which ishereby incorporated by reference in its entirety. In some embodiments,the surgery and/or other treatment plans are performed usingcomputer-guided surgical techniques that are based on the selectedtreatment option.

Referring to FIGS. 2 and 15, after executing the treatment plan or apart thereof at step 36, the method continues with step 38 in which apost-treatment analysis is performed. In some embodiments, thepost-treatment analysis is substantially similar to steps 22, 24, and/or26 described above. Referring more specifically to FIG. 15, showntherein is a flow chart illustrating the post-treatment analysis step 38of the method 20 according to one embodiment of the present disclosure.In that regard, the post-treatment analysis step 38 includes comparingthe predicted results of the modeling of step 30 to the actual resultsof the treatment at step 140. Any discrepancies between the model andthe actual results are identified at step 142. At step 144, thediscrepancies are utilized to improve the correlation between the modeland actual results. In that regard, the parameters utilized for creatingthe models are updated and modified based on the identifieddiscrepancies. Ideally, the predicted results provided by the model aresubstantially similar to the actual results of the treatment plan. Insome embodiments, the post-treatment analysis is performed at setintervals after the surgical procedure. In one particular embodiment,the patient goes through post-treatment analysis at least at 2 weeks, 6weeks, and 3 months after the surgical procedure. In some embodiments,sensors located within the implant and/or fixation devices are utilizedin the post-treatment analysis to obtain data related to the patient'smotion sequence(s).

By monitoring the resultant data from each patient for each treatmentplan, a statistical correlation between medical conditions and treatmentoptions is established. This statistical correlation is utilized inselecting the treatment plans for subsequent patients. For example, insome instances the method includes step 39 that comprises a feedbackloop to an earlier step in the method, such as step 30 for example. Inthat regard, modeling of the treatment options at step 30 can be updatedto correspond with the outcomes as observed in the post-treatmentanalysis of step 38. In that regard, in some instances the currentpatient's resultant data is routed and stored as a part of a studyand/or other collection of data into a database for future access by thesystem 10. Generally, the data will need to be de-identified from theparticular patient, so as to preserve confidentiality and impartially ofthe data and to comply with applicable laws. For example, the patient'sname, social security number, address, and/or other sensitiveinformation are removed from the data, while the patient's physicalcharacteristics, selected treatment plan, and outcome are maintained. Insome embodiments, the data is entered into the databases by a medicalprofessional as part of the post-treatment analysis of step 38 of themethod 20. This data related to current patient's outcome is thefeedback that provides confirmation of prior information and/or newinformation from which the medical professionals can modify thetreatment plans and/or medical device manufacturers can modify theimplants or devices.

Further, in some instances the database includes information regardingwhether the patient's treatment plan was an on-label or off-label use ofa medical product. In that regard, in some embodiments the databaseand/or software interface includes a field that allows the treatingphysician or medical personnel to describe the particular use of themedical product. Accordingly, a later physician can evaluate thepossibility of such a use for his or her patient. The database or systemcan highlight off-label uses so that treatment plans for later patientsare not adversely affected by previous off-label uses that skew the dataresults. In some embodiments, the database includes informationregarding reimbursement procedures. In that regard, the databaseincludes the various requirements for obtaining reimbursement fromvarious insurance companies in some embodiments. Further, the databasekeeps track of the success of previous reimbursement requests based onthe associated patient data in some instances. Accordingly, a treatingphysician is able to evaluate the likelihood of being reimbursed from aparticular insurance company for a selected treatment plan.

Referring again to FIGS. 1 and 2, the system 10 and, in particular, themodules 12, 14, 16, and 18 may provide a platform for executing some orall of the steps of the method 20 described above. Accordingly, aspectsof the system 10 will now be described in connection with the method 20.The diagnosis module 12 is adapted to execute some or all portions ofsteps 22, 24, and 38. In that regard, the diagnosis module 12 prompts auser to answer a series of diagnostic questions and/or provide one ormore menus for selecting items indicative of the patient's medicalcondition. The exemplary screen shot 40 of the software interface shownin FIG. 3 is utilized in some embodiments. The diagnosis module 12 isalso configured to process patient diagnosis data in addition to, or inlieu of, the diagnostic questions. In some embodiments, imagingtechniques are utilized to evaluate the patient and the diagnosis modulemay be adapted to receive, store, and/or process the images. Forexample, in some embodiments radiographic images of the patient'sanatomy are obtained, transferred to the diagnosis module 12, and storedin a database accessible by the diagnosis module 12. The radiographicimages are then analyzed by the diagnosis module 12 and/or the physicianto identify any medical conditions afflicting the patient. In someembodiments, the patient is put through a series of movementsappropriate to determine the patient's motion sequence and/or range ofmotion for one or more anatomical areas. The patient's motion sequenceand/or range of motion in each area are captured, transferred to thediagnosis module 12, and utilized by the diagnosis module in evaluatingthe patient. The diagnosis module 12 is configured to receive other datasets or information and take such data into account during theevaluation of the patient in some embodiments.

For example, the diagnosis module 12 is adapted to receive patient datarelated to an imaging study in some embodiments. The imaging studyincludes patient images obtained through the use of magnetic resonanceimaging (“MRI”), computed tomography (“CT”), video fluoroscopy, and/orother imaging techniques. In some embodiments, the imaging studyincludes techniques as described in commonly owned U.S. patentapplication Ser. No. 11/697,426 filed Apr. 6, 2007 and titled “Systemand Method for Patient Balance and Position Analysis”, hereinincorporated by reference in its entirety. In some embodiments, theimaging study of step 24 includes tracking the movement of anatomicalfeatures of the patient using sensors. The imaging data is stored in adatabase accessible by the diagnosis module 12.

Based on the response to the diagnostic questions and/or other types ofpatient analysis data obtained, the diagnosis module 12 groups thepatient into a particular classification of patient. In someembodiments, the classification is by type of injury or medicalcondition. In other embodiments, the classification is based on otherpatient factors such as height, weight, age, or otherwise. In someembodiments, the classification is at least partially based on atreating physician or other medical personnel's preferences that areselected or otherwise defined within the diagnosis module 12. It iscontemplated that in some instances each classification is furthersubdivided into groups based on factors such as the severity of thecondition, age, health, and/or other factors. In some embodiments, theclassifications and groupings are based on factors identified inclinical studies and/or past patient treatments as being indicators ofsuccess for the available treatment options. In that regard, thediagnosis module 12 is in communication with a database containinginformation regarding past clinical studies and/or patient treatmentsthat may be utilized in diagnosing the current patient.

The modeling module 14 is adapted to execute some or all portions ofsteps 26, 30, 32, 34, and 36 of the method 20. In that regard, themodeling module 14 synthesizes the information obtained by the diagnosismodule 12 during steps 22 and 24 to identify the abnormal medicalconditions afflicting the patient. In that regard, the modeling module14 creates a 3-D and/or 2-D animated model of the patient's anatomy. Theanimated model is based substantially on the imaging data obtained bythe diagnosis module 12. In some embodiments, the modeling module 14 isused to highlight the problem areas and/or times in the patient'sanatomical motion sequence. In that regard, the modeling module 14allows selection of particular layers of anatomical features. Forexample, in one embodiment the patient's motion anatomy is grouped intolayers according to types of anatomical tissue, such as bones,cartilage, ligaments, tendons, muscles, and/or combinations thereof. Themodeling module 14 provides a user interface allowing medical personnelto select the layers of anatomical tissue to be considered in modelingthe patient's motion. The modeling module 14 analyzes the motionaccording to the selected grouping of anatomical tissue and theinteractions therebetween.

In some embodiments, the modeling module 14 is configured to combinediagnostic tests with the imaging study in creating the animated model.For example, in some embodiments the modeling module 14 combines musclemonitoring with the imaging study to identify muscle contractions andtensions during a particular motion sequence or protocol. In otherembodiments, the modeling module 14 utilizes center-of-balance and/orcenter-of-gravity data for the patient obtained by the diagnosis module12. In some embodiments, devices and methods as described in commonlyowned U.S. Pat. No. 7,361,150 filed Jun. 25, 2004 and titled “Method andDevice for Evaluating the Balance Forces of the Skeleton,” hereinincorporated by reference in its entirety, are utilized. Musclemonitoring and center-of-balance data are merely examples of the typesof additional data that are used with the imaging data by the modelingmodule 14 in forming the animated model. In other embodiments, themodeling module 14 is adapted to utilize other types of the patient dataas well.

The modeling module 14 includes additional features to allow medicalpersonnel and/or a computer system to analyze the patient. In thatregard, in some embodiments the modeling module 14 creates a stress gridoverlay that highlights potential areas of increased stress or strain onthe patient's anatomy, such increased muscle activity; overstretching ofmuscles, ligaments, and/or tendons; friction between bones; and/or otherareas of stress/strain. In some embodiments, the module 14 provides auser interface that allows for zooming, panning, or otherwise changingthe orientation of the view of the patient's anatomy. A user adjusts theorientation to better observe or isolate a potential problem area.Similarly, in some embodiments the module 14 provides a user interfacethat allows a user to pause, rewind, slow down, and/or speed upsimulation of a motion sequence to better observe a potential problem.Further, the modeling module 14 allows 3-D and/or 2D tracking ofspecific anatomical features through the motion sequences in someinstances. In some embodiments, the modeling module 14 highlightspotential problem areas for the patient based on a comparison to astandardized model associated with the patient. In that regard, themodeling module 14 is in communication with a database containing aplurality of standardized models for such use. In some embodiments, theproblem areas are identified by the modeling module 14 by identifying anabnormal motion pattern.

The modeling module 14 is also utilized in modeling the selectedtreatment options. Modeling of the treatment options builds upon theanimated model of the patient used during the patient diagnosis andanalysis. Thus, in many aspects the module 14 utilizes the same featuresdescribed above in modeling the treatment options. However, in modelingthe treatment options the modeling module 14 modifies the model byreplacing a damaged portion of the patient's anatomy with an implant.The module 14 then utilizes the characteristics of the implant inmodeling the patient's anatomical motion sequences. Further, in someembodiments the modeling module 14 further modifies the model by takinginto consideration the surgical approach that will be used and anycorresponding anatomy that will be sacrificed by the surgical approach.In this manner, the modeling module 14 provides an estimation of theoutcome of the treatment plan taking into account these additionalfactors. In that regard, the treating physician may optimize eachtreatment plan by utilizing the modeling module 14 to modify suchfactors as the size, placement, orientation, and material properties ofa particular implant and/or modifying the surgical procedure to adjustthe tissues that will be compromised.

The modeling module 14 is also utilized in planning the selectedtreatment option in some instances. In some embodiments, the planningincludes determining an optimized surgical procedure for inserting theimplant. In that regard, the modeling module 14 takes into account suchfactors as the desired placement and orientation of the implant and/orthe need to preserve certain anatomical features in determining theappropriate surgical procedure. Further, in some embodiments desiredfixation positions and orientations for the fixation devices areestablished and marked on the model created by the modeling module 14.These fixation positions are saved for future reference during theactual surgical procedure. With respect to the planned placement andorientation of the implant and/or fixation devices, the modeling module14 establishes an error field that identifies the expected range ofaccuracy in which the implant and/or fixation devices will be implanted.Based on this expected range of accuracy, a corresponding range ofperformances are established for the treatment plan and modeled by themodeling module 14. For each of the selected treatment plans and/orimplants, the treating physician may save one or more optimized plans ina database or other memory accessible by the modeling module 14.

Subsequently, the optimized plans of the modeling module 14 are utilizedin the execution of the treatment plans. For example, the optimizedplans of the modeling module 14 are used during a surgical procedure toguide the physician to the appropriate placement of an implant and/orfixation device. The surgical procedure is monitored intra-operativelyand compared in real-time, or approximately thereto, to the plannedtreatment. Thus, the actual placement of the implant and fixationdevices is compared to the intended placement and/or the associatederror fields. In some embodiments, the position of the implant and/orfixation devices is established using implantable sensors located withinthe implant, fixation devices, and/or insertion instruments. In someembodiments, the surgery or other treatment plan is performed usingcomputer-guided surgical techniques that are based on the optimizedtreatment option created with the modeling module 14.

The treatment module 16 is adapted to execute some or all portions ofsteps 28, 30, 32, 34, and 36 of the method 20. In that regard, thetreatment module 16 identifies the available treatment options for aparticular patient. The treatment module 16 will identify the availabletreatment options based upon the patient analysis performed by thediagnosis module 12 and the modeling module 14. Thus, in someembodiments the treatment options may be determined by looking at theresults of the patient analysis, identifying the patient's medicalcondition(s), and proposing treatment plans based on the patient'smedical condition(s). The treatment module 16 may propose treatmentplans that include surgical procedures, non-invasive treatments, and/ormedicinal treatments. In some embodiments, the treatment module 16facilitates sorting and/or screening of the treatment options. In someembodiments, the treatment options are sorted and/or screened based onphysician preference. For example, if a physician prefers surgicalprocedures that utilize a posterior approach, then the availabletreatment options are limited to those implants and surgical proceduresthat may be implanted using a posterior approach. The treatment module16 also provides a user interface for selecting the physician'spreferences. As another example, in some instances the treatment optionsare sorted based on the success of the treatment plan for previouspatients having a similar profile to the current patient. Similarly, thetreatment options are sorted based on the previous procedures performedby the treating physician/surgeon and the relative success of thoseprocedures in some instances. In each of these examples, the treatmentmodule 16 is in communication with a database containing the relevantinformation for sorting and/or screening the treatment options. Forexample, in at least one embodiment the physician's preferences arestored in a database that is accessible by the treatment module 16 whenthe physician logs into the system using a username and password.Similarly, a database maintaining the results of the previous treatmentoptions and the patient details for these treatment options isaccessible by the treatment module 16 in some embodiments.

The post-treatment feedback module 18 is adapted to execute some or allportions of step 38 of the method 20. In some aspects the post-treatmentfeedback module 18 is substantially similar to the diagnosis module 12.In that regard, in some embodiments the system 10 does not include aseparate post-feedback module 18. Rather, the diagnosis module 12 andthe post-feedback module comprises a single module. The post-treatmentfeedback module 18 is utilized to compare the predicted results of themodeling module 14 to the actual results of the treatment plan. Anydiscrepancies between the model and the actual results are identified bythe post-treatment feedback module 18 and utilized to improve thecorrelation between the model and actual results. Ideally, the predictedresults provided by the model are substantially similar to the actualresults of the treatment plan. In some embodiments, sensors locatedwithin the implant and/or fixation devices are utilized by thepost-treatment feedback module 18 to obtain data related to thepatient's motion sequence(s). By monitoring the resultant data from eachpatient for each treatment plan, a statistical correlation betweenmedical conditions and treatment options is established. Thisstatistical correlation and/or underlying data are stored in a databaseaccessible by the diagnosis module 12, modeling module 14, and/or thetreatment module 16 and are utilized in conjunction with subsequentpatients for diagnosing, modeling, and/or selecting appropriatetreatment plans.

It is understood that while each of the modules 12, 14, 16, and 18 havebeen described as having particular functions no limitations areintended thereby. In that regard, the functions described above withrespect to a particular module may be performed by other modules and/ormultiple modules. In some embodiments the functions of two or more ofthe modules may be performed by a single module. In other embodiments,the function(s) of a single module may be distributed across multiplemodules. It is understood that the term module may include software,hardware, and/or combinations of hardware and software.

Referring now to FIG. 16, shown therein is an illustrative node 60 forimplementing embodiments of the systems and methods described above.Node 60 includes a microprocessor 62, an input device 64, a storagedevice 66, a video controller 68, a system memory 70, and a display 74,and a communication device 76 all interconnected by one or more buses72. The storage device 66 could be a floppy drive, hard drive, CD-ROM,optical drive, or any other form of storage device. In addition, thestorage device 66 may be capable of receiving a floppy disk, CD-ROM,DVD-ROM, or any other form of computer-readable medium that may containcomputer-executable instructions. Further communication device 76 couldbe a modem, network card, or any other device to enable the node tocommunicate with other nodes. It is understood that any node couldrepresent a plurality of interconnected (whether by intranet orInternet) computer systems, including without limitation, personalcomputers, mainframes, PDAs, and cell phones.

A computer system typically includes at least hardware capable ofexecuting machine readable instructions, as well as the software forexecuting acts (typically machine-readable instructions) that produce adesired result. In addition, a computer system may include hybrids ofhardware and software, as well as computer sub-systems.

Hardware generally includes at least processor-capable platforms, suchas client-machines (also known as personal computers or servers), andhand-held processing devices (such as smart phones, personal digitalassistants (PDAs), or personal computing devices (PCDs), for example).Further, hardware may include any physical device that is capable ofstoring machine-readable instructions, such as memory or other datastorage devices. Other forms of hardware include hardware sub-systems,including transfer devices such as modems, modem cards, ports, and portcards, for example.

Software includes any machine code stored in any memory medium, such asRAM or ROM, and machine code stored on other devices (such as floppydisks, flash memory, or a CD ROM, for example). Software may includesource or object code, for example. In addition, software encompassesany set of instructions capable of being executed in a client machine orserver.

Combinations of software and hardware could also be used for providingenhanced functionality and performance for certain embodiments of thepresent disclosure. One example is to directly manufacture softwarefunctions into a silicon chip. Accordingly, it should be understood thatcombinations of hardware and software are also included within thedefinition of a computer system and are thus envisioned by the presentdisclosure as possible equivalent structures and equivalent methods.

Computer-readable mediums include passive data storage, such as a randomaccess memory (RAM) as well as semi-permanent data storage such as acompact disk read only memory (CD-ROM). In addition, an embodiment ofthe present disclosure may be embodied in the RAM of a computer totransform a standard computer into a new specific computing machine.

Data structures are defined organizations of data that may enable anembodiment of the present disclosure. For example, a data structure mayprovide an organization of data, or an organization of executable code.Data signals could be carried across transmission mediums and store andtransport various data structures, and, thus, may be used to transportan embodiment of the present disclosure.

The system may be designed to work on any specific architecture. Forexample, the system may be executed on a single computer, local areanetworks, client-server networks, wide area networks, internets,hand-held and other portable and wireless devices and networks. In thatregard, it is understood that the network may be a secure network tocomply with patient confidentiality requirements and otherwise protectpatient data and/or proprietary information.

A database may be any standard or proprietary database software, such asOracle, Microsoft Access, SyBase, or DBase II, for example. The databasemay have fields, records, data, and other database elements that may beassociated through database specific software. Additionally, data may bemapped. Mapping is the process of associating one data entry withanother data entry. For example, the data contained in the location of acharacter file can be mapped to a field in a second table. The physicallocation of the database is not limiting, and the database may bedistributed. For example, the database may exist remotely from theserver, and run on a separate platform. Further, the database may beaccessible across the Internet. Note that more than one database may beimplemented.

When a surgeon has performed an orthopedic spinal treatment, the datafrom that treatment is commonly retained by the surgeon and, in manycases, is submitted to studies of various types for assimilation. Thedata may be gathered and stored in a database accessible by the modulesof the system 10 for future access and analysis. The data may be sortedand organized in various hierarchies. For example, a first level mayinclude generally all received data without limiting the outcomes orpatient pathology by type of outcome, anatomical area of treatment, orother specific category. A second level, for example, may filter thegeneral data down to data or studies obtained by various study groups.For example, a spine trauma study group (STSG) may focus on injury tospinal region such as the vertebrae and associated tissue. A thirdlevel, may filter data within each of the various study groups. Forexample, continuing the STSG example, the spinal data may be filtereddown to data or studies to a particular region of the spine, such as alumbar spine study group (LSSG), which may focus on treatments andoutcomes in solely the lower back (e.g. lumbar and sacral vertebrae andassociated tissue). Similarly, another grouping may filter the spinaldata down to data or studies by a cervical spine study group (CSSG),which may concentrate on outcomes and treatments in the upper vertebralregion, including the neck and occiput. Yet another grouping may filterthe spinal data down to data or studies by a spinal deformity studygroup (SDSG), which may study and report data concerning treatment ofscoliosis and other deformative conditions. Numerous other groupings anddivisions may be created to further subdivide the data and studies ofthe various study groups. The data collected by the surgeon or his orher team at each level may include a variety of numerical, language,image (e.g. radiographic) or other data, and can include data sufficientto perform the related surgical operations. Instructions or training asto the data to be provided by the surgeon or his or her team may beprovided by a user interface and/or personal training.

The data from these studies can be provided to one or more softwareapplications in order to derive further information or new views of thedata. Examples of such software include the Montreal 3D RadiographicModeling, Measurement and Surgery Simulation software (“Montrealsoftware”); the TruBalance patient measurement software (“TruBalancesoftware”); and the DRPro radiographic measurement software offered byPhDx eSystems, Inc. of Albuquerque. Other brands or types of softwarefor obtaining, analyzing, or otherwise handling patient data may be usedin addition to or instead of one or more of the software applicationsmentioned above for one or more of the data categories. Also, multiplesoftware applications may be applied to a given set of data. It isunderstood that data from each study can be assembled together prior tosubmission to such software, or each study can be treated individually.

Output from the software applications may be routed to one or moredatabases for storage. These databases may be physically distant fromeach other, but may be connected via electronic connections, such ashard-wired connections, internet or other network connections, and/orsatellite connections. In that regard, in some embodiments the databasesmay be directly accessible by the modules 12, 14, 16, and 18 of thesystem 10 for use in the patient diagnosis, analysis, and treatmentplanning. In other instances, data from these databases and othersources may be compiled to create a master database. It is understoodthe data need not necessarily reside on a single server or hard-drivesystem to form the master database. Rather, the databases containingdata from each of the studies may feed data into or be accessible viathe master database. Further, it is understood that data may be passedto other databases or outputs. Data or other outputs from thedatabase(s) may be output in the form of a report. The report may be ina computer readable form and/or in human intelligible form. In thatregard, the report may be utilized by the system 10 and/or a treatingphysician in determining appropriate treatment options for a patient.

Medical professionals, for example spinal surgeons, may access thedatabase(s) via a software interface or computer network. Thisassessment, treatment and outcomes modeling software of the system 10allows entry of data of a current patient for which a diagnosis and/ortreatment options and analysis is desired. That data may then becompared to the data available in or through the accessible database(s).If necessary, the data obtained from or through database(s) may bebuffered or otherwise copied and transformed, e.g. via mathematical orother algorithm, so that it can be more efficiently compared to thecurrent patient data via human or machine review. The comparisons and/ortransformations may be made using the IRT models or equations, incertain embodiments. Such a comparison can be used to obtain records ofprevious medical cases in which patients having similar characteristics(e.g. gender, height, weight, age or affliction) were treated withvarious treatments, and their corresponding outcomes. In this way, themedical professional can quickly obtain a view of outcomes of one ormore treatments for his or her current patient, and/or the likelihood ofa positive outcome for a given treatment. With this information and hisor her personal knowledge and experience, the professional can come to atreatment decision or recommendation more quickly, more efficiently, andwith greater likelihood of successful treatment outcome.

Other uses can also be made of the outputs of the database(s). Inaddition to medical professionals studying previous outcomes forguidance on a current patient's case, bioengineers can study outcomeshaving to do with particular products or afflictions toward improvingexisting implants or other devices and/or creating new devices andtreatment plans. Further, as a repository of clinical and treatmentinformation, the database(s) may also be used by historians,epidemiologists, or others with an interest in such data.

In addition to data communication between medical personnel (physicians,surgeons, medical assistants, etc.), patients, and/or medical devicemanufacturers, the system 10 may also provide a communication link to atreatment facility. In one embodiment, the system 10 is in communicationwith a physical therapy treatment facility such that the treatingphysical therapist may. In other embodiments, the system 10 may be incommunication with other treatment facilities or rehab centers. Aspost-operative therapies can play a significant role in the overalleffectiveness of a surgical procedure, in some embodimentspost-operative therapies may be a component of the available treatmentplans for the patient. In other embodiments, the system 10 may include aseparate module for determination of appropriate post-operativetherapies based on the treatment plan selected for the patient and/orother factors.

Among other things, there is disclosed a system for use by medicalprofessionals and other experts for assisting them in making decisionson treatments aimed at the desired clinical outcome for the patient.Such systems may include methods for coordinating a number of sources ofdata and assimilating relevant data to provide actionable informationfor the professional. Using such information with the appropriateconfidence intervals, a surgeon (for example) can come to a clinicaldecision on treatment based on data of actual patient outcomes relatingto identical or similar treatments. In particular embodiments, the dataanalysis can take the form of an item response theory (IRT) modelizationtechnique that allows a professional to compare his or her patient'sdata to actual previous patient outcomes data that have been conditionedwith a probabilistic treatment result. The system may perform in amethodical and predictable way one or more of the variable or“trade-off” decisions among possible treatment options normally made byskilled professionals.

The methods and systems disclosed herein were originally developed foruse with orthopedic surgery cases, and in particular with orthopedictreatments of the spine. Accordingly, the following description will usethe context of spinal orthopedic medicine and treatments used therein.It is to be understood that identical or similar methods or systemscould be used in other medical fields, such as cardiology or oncology.

Referring generally to FIG. 17, shown therein is a flow chart of anembodiment of a data flow method according to another aspect of thepresent disclosure. As indicated, this embodiment reflects storage,movement and usage of data obtained from treatment of patients. Block220 reflects that treatment, which in the spinal orthopedic field mayinclude open or minimally-invasive surgery, stabilization throughimplantation of rods, plates and/or disc prostheses, fusion of one ormore vertebral levels via intervertebral cages, placement of osteogenicmaterials, or many other procedures. The decision as to what treatmentto use may come from a surgeon's fundamental knowledge of biology,particularly anatomy, and current and past basic research in the field.The decision may also be influenced via reports or other information ofresults from other surgeries or treatments, as indicated at blocks 222and 224, and further discussed below.

When a surgeon has performed an orthopedic spinal treatment, the datafrom that treatment is commonly retained by the surgeon and, in manycases, is submitted to studies of various types for assimilation. Blocks226, 228, 230, 232, and 234 indicate such gathering by entities into arepository of outcomes for access and analysis. Block 226 reflects agathering of outcomes generally, without necessarily limiting outcomesor patient pathology by type of outcome, anatomical area of treatment,or other specific category. Block 228 reflects studies by a study groupA. In one exemplary embodiment, study group A is a spine trauma studygroup (STSG) that focuses on injuries to vertebrae and associatedtissue. Block 230 reflects studies by a study group B. In one exemplaryembodiment, study group B is a lumbar spine study group (LSSG), whichfocuses on treatments and/or outcomes in the lower back, (e.g. lumbarand sacral vertebrae and associated tissue). Block 232 reflects acollection of data by a study group C. In one exemplary embodiment,study group C is a cervical spine study group (CSSG), which concentrateson outcomes and treatments in the upper vertebral region, including theneck and occiput. Block 234 shows data collection by a study group D,which in one exemplary embodiment is a spinal deformity study group(SDSG) that focuses on the treatment of scoliosis and other deformativeconditions. The data collected by the study groups A, B, C, and D caninclude a variety of numerical, language, image (e.g. radiographic) orother data, and can include data sufficient to perform the operationsnoted below. Instructions or training as to the data to be collected bya surgeon or his or her team working within each of the study groups A,B, C, and D is provided in some instances to ensure that all or at leasta majority of the pertinent information is collected.

The data from these studies can be provided to one or more softwareapplications in order to derive further information or new views of thedata. Examples of such software are indicated in blocks, 236, 238, and240. Block 236 shows a 3D modeling, measurement, and simulationsoftware. In some instances, the 3D modeling, measurement, andsimulation software is the Montreal 3D Radiographic Modeling,Measurement, and Surgery Simulation software (“Montreal software”). TheMontreal software takes provided data and provides outputs withradiographic models or other images, provides measurements relevant tothe anatomy and procedure, and can create a simulation of surgicalprocedure(s). Block 238 shows a patient measurement software. In someinstances, the patient measurement software is configured to obtainpatient balance information, including center-of-balance information. Insome embodiments, TruBalance patient measurement software (“TruBalancesoftware”) is utilized. Block 140 shows a radiographic measurementsoftware that is utilized to obtain measurements of the patient'sanatomical features from a radiographic image. In some instances, DRProradiographic measurement software offered by PhDx eSystems, Inc. ofAlbuquerque is utilized. The DRPro software can be used to measure andotherwise obtain data from radiographs or other images. Other brands ortypes of software for obtaining, analyzing, or otherwise handlingpatient data could be used in addition to or instead of one or more ofthe software applications described above one or more data categories.Also, multiple software applications may be applied to a given set ofdata. In the embodiment of FIG. 17, as one example, output or data fromany of the study groups of blocks 226, 228, 230, 232, and 234 can berouted for handling by the radiographic measurement software of block240. It will be seen that data from each study can be assembled togetherprior to submission to such software, or each study can be treatedindividually.

As seen in embodiment of FIG. 17, output from blocks 236, 238, and 240may be routed to databases for storage. Block 242 indicates a 3-DModeling, Measurement, and Simulation Study Data and Image Store thatincludes output of models, measurements, simulations and other images orinformation from the 3-D modeling, measurement, and simulation software(block 236). Block 244 indicates a Patient Measurement Study Data andImage Store that includes output of the information from the patientmeasurement software (block 238). Block 246 indicated a RadiographicMeasurement Study Data and Image Store that includes radiographinformation, study data, and/or other information from the radiographicmeasurement software (block 240) and/or from studies, such as thoseindicated in blocks 226, 228, 230, 232, and 234. These databases may bephysically distant from each other, but may be connected via electronicconnections, such as hard-wired connections, internet or other networkconnections (wired or wireless), and/or satellite connections.

Data from these databases and other sources can be brought together to amaster database, shown in block 250 and labeled “Spine Registry DataMart.” Block 250 brings together data from blocks 242, 244, and 246. Thedata from the blocks 242, 244, and 246 is aggregated into the databaseof block 250 in some instances. In other instances, the data from blocks242, 244, and 246 is accessible from the database of block 250, but notnecessarily part of the database of block 250. In the illustratedembodiment, one or more independent databases shown in block 251 areincluded in or accessible by the master database of block 250. As shownthe independent databases 251 are available to the master database viaone or more Independent Study Data Stores, shown in block 252. It willbe seen that other databases, e.g. databases dedicated to data fromother studies noted above, could also feed data into or be accessiblevia the database in block 250. In some instances, the independentdatabases include the Scolisoft database or the Spine Tango database.Further, it will also be seen that data can be passed to other databasesor outputs. As shown in FIG. 17, data or other output from block 246 maybe output in the form of reports, shown generally at block 224. Asindicated above, physicians in the course of considering or givingtreatment (block 220) may consult such reports.

Medical professionals, for example spinal surgeons, can access thisdatabase in block 250 via the software in block 254, labeled “SpineATOM.” This assessment, treatment and outcomes modeling software allowsentry of data of a current patient for which a diagnosis and/ortreatment options and analysis is desired. That data is then compared tothe data available in or through database 250. If necessary, the dataobtained from or through database 250 may be buffered or otherwisecopied and transformed, e.g. via mathematical or other algorithm, sothat it can be more efficiently compared to the current patient data viahuman or machine review. The comparisons and/or transformations may bemade using the IRT models or equations, in certain embodiments. Such acomparison can be used to obtain records of previous medical cases inwhich patients having similar characteristics (e.g. gender, height,weight, age or affliction) were treated with various treatments, andtheir outcomes. In this way, the medical professional can quickly obtaina view of outcomes of one or more treatments for his or her currentpatient, and/or the likelihood of a positive outcome for a giventreatment (block 256). With this information and his or her personalknowledge and experience, the professional can come to a treatmentdecision or recommendation more quickly, more efficiently, and withgreater likelihood of successful treatment outcome. As indicated in FIG.17, the study of outcomes (block 256) can translate into reports ofother information (block 222) that assist the physician in consideringoptions or planning treatment.

Other uses can also be made of the output of the ATOM analysis. Inaddition to medical professionals studying previous outcomes forguidance on a current patient's case, bioengineers can study outcomeshaving to do with particular products or afflictions toward improvingexisting implants or other devices or creating new devices andtreatments (block 258). Blocks 260 and 262 represent uses by managementto review outcomes for market, treatment and other trends. The databased on blocks 258, 260, and 262 and/or the decisions resultingtherefrom are utilized in developing new products and modifying existingproducts in the product pipeline shown in block 264. As a repository ofclinical and treatment information, a database such as that shown inblock 250 could also be used by historians, epidemiologists, or otherswith an interest in such data.

Referring now generally to FIG. 18, shown therein is an embodiment of apatient diagnostic model according to another aspect of the presentdisclosure. The model begins generally when medical professional(s)consult with a patient (block 266), either as an initial appointment orthrough a referral. Again using the context of spinal surgery solely forillustration, at or after such consultation both the patient (block 268)and the professional(s) (block 270) provide information on study formsor in other ways. Such information may include standard information onthe physical characteristics and condition of the patient. In aparticular embodiment, such information may also include a series ofoptions for the patient and/or the medical professional to select. Suchoptions are geared toward helping to determine and appropriatetreatment, as disclosed herein, and could include goals as topost-operative mobility, activity or relative deformity, pre-operativecondition, prior surgeries or other treatments, or other factors. Theinformation may also include weighting or come or all factors so as toemphasize one or more factors as the analysis of potential treatments isperformed.

In addition, radiographs (e.g. x-rays, MRI images, CT scans) or otherimages can be taken of the current patient (block 272). Data from theseimages are taken via software, and in the illustrated embodiment 3-Dmodeling, measurement, and simulation software is used to generate athree-dimensional model of the patient's spine (block 274), patientmeasurement software is used to calculate a global balance (block 276),and radiographic measurement software is used to measure the images(block 278). An additional step that can be used is to measure theradiographs and/or the 3-D images generated by the software at block 274with additional measurement software (block 280). In some instances,software known as Clindexia is utilized at block 280. Generally, thesesoftware applications of blocks 274, 276, 278, and 280 transform the rawimages into mathematical or other forms that can easily be compared viaa computer or similar machine to images or other data from a database(e.g. database 250 of FIG. 17) that have been similarly transformed.

The information from the radiographs or other images and the informationfrom the patient's and medical professional's study forms are combinedinto a file or database (block 282) in the illustrated embodiment. Thatpatient's clinical assessment data then be used to help the medicalprofessional(s) to select an appropriate treatment, as described above.For example, some or all of the data can be stored in a local file, discor server (block 284), so that it can be accessed easily by a computeror other processor that is also able to access the information availablein or through database 250. Block 286 reflects the analytical process.Two subprocesses are shown in block 286, the first of which is the ATOMprocess of comparing the current patient's data to aggregate data ofother patients and their treatments and outcomes. The second subprocessshown in block 286 is a simulation of surgery based on the currentpatient's data, including weighted factors concerning the patient'scharacteristics or desired outcome. This surgical simulation isperformed with a software application in some instances. In oneparticular embodiment, software known as S3 Spine Surgery Simulator isutilized. Using these subprocesses, a professional can select a possibletreatment based on the comparison of his or her patient'scharacteristics and weighted factors to previous patients, treatmentsand outcomes, and simulate that treatment to calculate the likelihood ofa successful outcome (as suggested by the data collected in blocks 270and 272).

One or both subprocesses shown in block 286 may be used, and either orboth may be used multiple times as the surgeon or other medicalprofessional may desire, so that the professional can evaluate as manytreatment scenarios as he or she deems appropriate. Once thesubprocesses have been run, the professional can select a treatment(block 288) that best meets the patient's characteristics, afflictionand stated goals or weighted outcome factors, and appears to be mostlikely to achieve the desired outcome. That selection is used inchoosing implants, devices, compositions, and other products for thetreatment (block 290), in preparing for an implementing the treatmentand obtaining an outcome (block 292), and in developing data from thetreatment and the outcome (block 294).

Block 296 in the embodiment of FIG. 18 indicates the routing and storageof the current patient's data as a part of studies and/or othercollection of data into databases such as those noted above with respectto FIG. 17. Once that data is appropriately de-identified with theparticular patient, so as to preserve confidentiality and impartially ofthe data, and to comply with applicable laws, it can optionally beentered into such databases, e.g. following block 282. As discussedabove, data from those databases is used in at least the comparison(s)performed at block 286. Additionally, the data obtained in block 294 ofthe outcome of the current patient's procedure can be transferred tosuch databases, again after being appropriately de-identified. This newdata is the feedback to the databases that provides confirmation ofprior information and/or new information from which professionals canlearn in the future.

FIG. 19 shows schematically an embodiment of a system that is used toperform the methods disclosed herein according to one aspect of thepresent disclosure. A processor or central processing unit 297 is shownelectronically linked to one or more databases 298 and to one or moreinput/output devices 299. More than one processor may be used, in theform of one or more computers or other devices, or a single processormay be programmed to accomplish tasks discussed herein. Processor 297may be a part of a network, such as the internet. Databases such asthose described above may be individually linked to processor 297, asthe line between blocks 297 and 298 suggests, or may be physically orelectronically combined so that a single electronic link exists betweenprocessor 297 and database(s) 298. Input/output devices 299 may bephysically proximate or remote items such as disc drives, monitors,printers, or other devices for inputting and outputting information fromprocessor(s) 297. Thus, current patient information may be inputted viainput/output device(s) 299 to processor(s) 297, which can compare thatinformation (as discussed herein) to data from database(s) 298. Anoutput of the comparison(s) may be received via input/output device(s)299. Processor(s) 297 may also be programmed to perform treatmentsimulations (as discussed herein), again with output being received viainput/output device(s) 299.

The methods described above can be performed in any of a variety ofways. In one embodiment, the data are transferred to electronic media,is they are not taken or recorded immediately in that form, and aresimilarly stored in such media. The various databases and softwarediscussed above may be available at a single geographic location, or maybe linked together electronically or simply accessible by appropriateelectronic devices. As one example, the databases may be accessible to aparticular computer via a network, such as intranet, a dedicatednetwork, or the internet, and the particular computer may have thesoftware necessary to access the data and make the comparisons andanalyses noted above.

With the above described embodiments, an algorithm, or expert system toprovide medical personnel involved with patient care a modelizationtechnique for optimizing the treatment of the patient. Such optimizationis created through assessment factors (e.g. characteristics of thepatient and desired goals), treatment factors (e.g. efficacy orinvasiveness), and outcome factors (e.g. desired post-operativecondition) relative to a pathology of a patient's condition. Such atreatment algorithm can be derived from a compilation of aggregatedstudy data to which weighted factors selected by medical professionalsare applied. The weighted factors are provided as options in answeringquestions in one or more study questionnaires. From those weightedfactors and the aggregated data, it is contemplated that medicalprofessionals may identify a representative or simulated patient fromwithin the aggregated data set. Once that particular patient or datasimulating a particular patient is found, the professional may model avariety of clinical outcomes parameters based on a set of initialconditions and proposed treatment alternatives. Each proposed treatmentalternative will provide a likely outcome or a range of likely outcomes,and the medical professional can evaluate the treatment alternatives andtheir risks and rewards. With the clinical outcomes modeled and theresults displayed with respective confidence intervals or levels foreach outcome related to the particular treatment options, the medicalpersonnel can make the appropriate decisions for treating the patientwith a balanced trade-off of parameters important to them. Importantly,the medical personnel's decision can be made from actual patient datarelative to a similar condition that represents their particularpatient's problem.

Referring now to FIGS. 20-22, shown therein is are methods for obtainingand analyzing patient information for diagnosing and treating a patientaccording to another embodiment of the present disclosure. Referringmore specifically to FIG. 20, shown therein is a flow chart illustratinga method of collecting and assessing data associated with diagnosing apatient and selecting available treatment options for the patientaccording to another embodiment of the present disclosure.

Referring more specifically to FIG. 20, shown therein is a flow chartillustrating a method 300 for diagnosing a patient, identifyingavailable treatment options for the patient, selecting a treatmentoption for the patient, and performing the selected treatment optionaccording to another embodiment of the present disclosure. The method300 begins at step 302 when the patient enters with complaintsindicative of a medical condition. Based on the types of complaints thepatient has, the method 300 continues at step 304 by categorizing thepatient. In that regard, in some embodiments categorizing the patientcomprises identifying one or more predetermined categories that areassociated with the symptoms or complaints indicated by the patient. Thepredetermined categories are provided to the treating physician in someinstances. In other instances, the treating physician or other medicalpersonnel at least partially defines the categories. In someembodiments, the categories are at least partially defined or organizedas set forth in FIG. 22 discussed in greater detail below. Generally,each category defines a series or set of data points that are useful inevaluating the patient. For example, in some embodiments where a patientcomplains of pain in a bony region, the data set defined by the categoryincludes obtaining an x-ray of the problem area. Similar correlationsbetween the patient's symptoms and the desired medical informationand/or tests associated with that symptom are defined for each category.

After categorizing the patient at step 304, the method 300 continues atstep 306 with collecting the data associated with each category in whichthe patient has been categorized. Accordingly, the extent of the datacollection will vary depending on the categorization of the patient atstep 304. In some instances, at least some of the data collection isprovided by the patient's primary care physician or referring physicianas indicated by step 308. In that regard, the patient has oftenpreviously undergone testing and/or imaging included in thecategorization data. In some instances, this information is providedfrom the prior medical personnel to the current medical personnel over atelecommunications network, such as the internet, phone system, fax, orotherwise. In one particular embodiment, the data is stored in adatabase accessible by the current medical personnel.

In addition to any data that is available from previous medicalpersonnel, the remaining data that is suggested to be collected for eachcategory is obtained from the patient. The data may include standardinformation on the current physical characteristics (e.g., height,weight, mobility, etc.) and condition of the patient. Further, the datamay include goals as to post-treatment mobility, activity, or relativedeformity of the patient. At least some of the data is obtained bydetermining the answers to a series of diagnostic questions defined byeach category. In one particular category, the diagnostic questionsinclude questions such as: How far can the patient walk without pain?Does the patient have pain lying down? Does the patient have painsitting? Does the patient have pain standing? Does the patient have backpain with leg pain? If yes, is the leg pain localized or radiating?These are exemplary questions and are not to be considered limiting.Numerous other questions may be utilized depending on the categorizationof the patient. In addition, the questions within each category may benested such that subsequent questions depend on the answers to previousquestions. Further, some or all of the questions may be weighted so asto emphasize one or more factors associated with a category. That is,particular questions and the resultant information provided by theanswers to those questions are given more importance than otherquestions and answers. In that regard, some questions and/or data willbe optional for a particular category. In some instances, the treatingphysician or medical personnel may determine the questions and/or datato be included in each category. In some embodiments, the questions ofassociated with each category are provided to the patient and/or medicalpersonnel in an interactive computer program.

In addition to or in lieu of the diagnostic questions for each category,the data collection step 306 may include other types of patient analysisdepending on the category. For example, in some embodiments imagingtechniques are utilized to obtain additional data regarding the patient.For example, in some embodiments radiographic images of the patient'sanatomy are obtained. The radiographic images are then analyzed toidentify the relevant data associated with the categorization of thepatient. In some embodiments, the patient's motion sequence and/or rangeof motion in one or more anatomical areas is a data point to beconsidered in evaluating the patient. Accordingly, in some embodimentsthe patient is put through a series movements appropriate to determinethe patient's motion sequence and/or range of motion in the one or moreanatomical areas.

In other embodiments, the imaging study includes obtaining patientimages through the use of magnetic resonance imaging (“MRI”), computedtomography (“CT”), video fluoroscopy, and/or other imaging techniques.In some embodiments, the imaging study includes techniques as describedin commonly owned U.S. patent application Ser. No. 11/697,426 filed Apr.6, 2007 and titled “System and Method for Patient Balance and PositionAnalysis”, herein incorporated by reference in its entirety. In general,the imaging study obtains images of the patient's anatomy that areutilized to obtain data points as suggested by the categorization of thepatient in step 304.

In some embodiments, the imaging study includes tracking the movement ofanatomical features of the patient using sensors. In some embodimentsthe sensors are implantable and are placed in direct contact with and/orwithin the relevant anatomical feature(s) of the patient. In otherembodiments, the sensors remain outside of the patient's body, but arepositioned in close proximity to the anatomical feature(s) of interest.For example, in some embodiments the imaging study tracks the positionof at least some of a patient's vertebrae. In one embodiment, a sensoris implanted into each vertebra and the location of the vertebra istracked using the sensor. In another embodiment, a sensor is placedoutside the patient's body adjacent the spinous process. The location ofthe spinous process and, in turn, the vertebra are tracked using thesensor. In some embodiments, the position of the sensors and anatomicalfeatures are tracked while the patient is put through a particularmotion sequence or protocol. For example, in one embodiment the patientis asked to walk on a treadmill. The position of the sensors andanatomical features are tracked and correlated to the patient's gaitcycle. It is understood that these described uses of sensors are merelyexemplary and should not be considered limiting. Sensors, implantable orotherwise, may be utilized in numerous other combinations and ways totrack the position of anatomical features during the imaging study.

Additional questions, imaging, and/or tests are utilized to obtain dataregarding the patient during step 306 as determined by the treatingphysician or other medical personnel.

After the relevant data has been collected at step 306, the method 300continues at step 310 by providing the data to one or more softwareapplications. In some embodiments, the answers to any questions promptedby the categorizations are input directly into the relevant softwareapplication. With respect to the imaging data, in some embodiments thedata from the imaging study is provided to one or more softwareapplications in order to derive further information and/or new views ofthe imaging data. Various brands or types of software for obtaining,analyzing, or otherwise handling patient data may be used for one ormore of the data categories. Also, multiple software applications may beapplied to a given set of data. It is understood that data from eachstudy can be assembled together prior to submission to such software, oreach study can be treated individually. In some embodiments, theMontreal software is used to generate a three-dimensional model of thepatient's spine, the TruBalance software is used to calculate a globalbalance for the patient, and the DRPro software is used to measure theimages. In some embodiments, the images are provided to software knownas Clindexia for measuring the images. These software applications cantransform the raw images into mathematical or other forms that can beutilized by other software applications and/or manipulated via acomputer system and compared to other images and/or other data sets.

After the data has been provided to the respective softwareapplication(s) at step 310, the method 300 continues with step 312 inwhich the software application(s) analyzes the data. Generally, thesoftware application synthesizes the information obtained in step 306 toidentify any abnormal medical conditions afflicting the patient. In someembodiments, the analysis of the data includes creating a 3-D and/or 2-Danimated model of the patient's anatomy. This model may be visuallyrepresented, such as on a computer screen or otherwise, in someembodiments. Generally, the animated model is substantially based on thedata obtained in step 306. In some embodiments, the animated model isused to highlight the problem areas and/or times in the patient'sanatomical motion sequence. In that regard, in some instances the modelincludes layers of anatomical features that are selectively included orremoved. For example, in one embodiment the patient's motion anatomy isgrouped into layers according to the various types of anatomical tissue,such as bones, cartilage, ligaments, tendons, muscles, and/orcombinations thereof. The animated model then analyzes motion accordingto each grouping of anatomical tissue and the interactions therebetween.

In some embodiments, the animated model combines diagnostic tests withthe imaging study. For example, in some embodiments the animated modelcombines muscle monitoring with the imaging study to identify musclecontractions and tensions during a motion sequence or protocol. Theresults of the muscle monitoring are combined with the other imagingdata to provide additional details and/or realism to the animated model.In other embodiments, the animated model utilizes center-of-balance orcenter-of-gravity data for the patient obtained during the motionsequence or protocol. Muscle monitoring and center-of-balance data aremerely examples of the types of additional data that may be combinedwith the imaging data in forming the animated model. Other types of thepatient data may also be utilized. In that regard, in some embodimentsthe treating physician or medical personnel selects the types of patientdata to be used in formulating the animated model.

The animated model includes additional features to allow medicalpersonnel and/or a computer system to analyze the patient. In thatregard, in some embodiments the animated model includes a stress gridoverlay that indicates potential areas of increased stress or strain onthe patient's anatomy, such increased muscle activity; overstretching ofmuscles, ligaments, and/or tendons; friction between bones; and/or otherareas of stress/strain. In some embodiments the model allows forzooming, panning, or otherwise changing the orientation of the view ofthe patient's anatomy. Users can adjust the orientation of the modelrelative to particular anatomical features to better observe or isolatea potential problem area. Similarly, the animated model allows a user topause, rewind, slow down, and/or speed up simulation of a motionsequence to better observe a potential problem. Further, the animatedmodel allows 3-D and/or 2D tracking of specific anatomical featuresthrough the motion sequences. In some embodiments, the softwareapplication that creates the animated model also highlights potentialproblem areas automatically based on a comparison to a standardizedmodel. In other embodiments, the treating physician or medical personnelnotes the potential problem areas based on their own observations. Insome embodiments, the problem areas are identified by the softwareapplication and/or medical personnel by recognizing an abnormal motionpattern(s). In some embodiments, the model is utilized internally by thesoftware application to identify the patient's potential medicalproblems, but no visual representation of the model is created.

After the software application analyzes the data at step 312, the method300 continues with step 314 where an analysis summary and accompanyingstatistics are provided. Generally, the summary provides informationimportant to the diagnosis and subsequent treatment of the patient'smedical condition. In some embodiments, the summary identifies suchthings as damaged anatomical features or areas, limited ranges ofmotion, and/or other data related to the patient's medical condition.The information provided by the summary is at least partially determinedby categorization of the patient in step 304 and may be further definedby the medical personnel. For example, in some embodiments the medicalpersonnel selects or otherwise accesses the particular information ordata sets they deem to be most important in diagnosing and treating thepatient. The statistical summary also provides a list of possible causesfor the medical condition and/or identifies possible relationshipsbetween abnormal motion patterns in some embodiments.

After the analysis summary has been provided at step 314, the method 300continues with step 316 where the analysis summary is compared to aprior patient data set. In that regard, there are multiple types ofprior patient data sets that may be used. The particular prior patientdata set utilized is determined by the availability of the data setsand/or physician preference. In some embodiments, the multiple priorpatient data sets are groupings within a single larger data set. Inother embodiments, the prior patient data sets are unrelated, individualdata sets. Examples of the different types of prior patient data setsinclude a particular physician's own prior patients; an aggregatedcollection of patients from multiple physicians, hospitals, and/orstudies; patients from specific medical personnel, such as a renownedphysician, a mentor, a consultant, or other medical personnel; and/or apatient wizard using a probabilistic matching system (i.e., grouping ofpatients with similar attributes to the current patient). In someembodiments, the treating physician or other medical personnel at leastpartially defines or selects the parameters of the prior patient dataset to be used. In some embodiments, the prior patient data set is acollection of prior patients having similar medical conditions, medicalhistories, and/or patient profiles to the current patient. The priorpatient data sets include the selected treatment plans and relativesuccess of those plans for the prior patients. Accordingly, the currentpatient's physical characteristics and attributes can be compared toprior patients with similar characteristics and attributes. Then, theone or more treatment options that have been successful for priorpatients with characteristics and attributes similar to the currentpatient may be identified.

After the patient analysis summary has been compared to the priorpatient data set(s) at step 316, the method 300 continues with theselection of a appropriate treatment option(s) at step 318. In someembodiments, the comparison of the patient analysis summary and theprior patient data will identify a single treatment plan that is clearlyconsidered best for the patient. However, in other embodiments aplurality of treatment plans are identified by the comparison aspossible treatment plans for the current patient. In such embodiments,the plurality of treatment options are sorted and/or screened to furthernarrow the treatment options. In some instances, the treatment optionsare screened based on physician preference. For example, if a physicianprefers a particular surgical procedures or approach, then the availabletreatment options are limited to those that utilize the preferredprocedure or approach. As another example, the treatment options aresorted or ranked based on the likelihood of success of the treatmentplan based on the data for previous patients having a similar profile tothe current patient. Similarly, in some embodiments the treatmentoptions are sorted or ranked based on the previous procedures performedby the treating physician/surgeon and the relative success of thoseprocedures. Based on the comparisons to prior patient data, physicianpreferences, and likelihood of success a specific treatment plan isselected for the patient.

Referring more specifically to FIG. 21, shown therein is a flow chartillustrating a method 320 for data collection and analysis according toanother embodiment of the present disclosure that is used in conjunctionwith a method of diagnosing a patient, identifying available treatmentoptions for the patient, selecting a treatment option for the patient,and performing the selected treatment option, such as method 300described above. In that regard, some of the steps of the method 320 aresubstantially similar to some of the steps of the method 300 where thesteps of the method 300 are focused on data collection and/or dataanalysis.

The method 320 begins at step 322 when the patient enters withcomplaints indicative of a medical condition. The method 320 continuesat step 324 where the patient is asked a series of questions and/or isotherwise assessed. Generally, the questions and/or assessment will befocused around the physical symptoms associated with the patient'scomplaints. These initial questions are focused on identifying potentialmedical problems of the patient. Additional questions and informationwill be obtained based on the answers to the questions of step 324. Forexample, in the current embodiment the method 320 continues at step 326where the appropriate diagnostic recommendations are determined. Thediagnostic recommendations comprise the medical tests, imaging, and/oradditional questions that the patient should be put through based on theanswers to the initial questions/assessment of step 324.

In some embodiments, the diagnostic recommendations are based ongrouping the patient into categories based on the responses of step 324.In that regard, in some embodiments categorizing the patient comprisesidentifying one or more predetermined categories that are associatedwith the symptoms or complaints indicated by the patient. Thepredetermined categories are provided to the treating physician in someinstances. In other instances, the treating physician or other medicalpersonnel at least partially define the categories. In some embodiments,the categories are at least partially defined or organized as set forthin FIG. 22.

Referring more specifically to FIG. 22, shown therein is a diagrammaticschematic view of a data structure 342 for use with the methods 300, 320according to one embodiment of the present disclosure. Generally, thedata structure 342 comprises a series of categories 344 that each definea corresponding data set 346. Each data set 346, in turn, comprises aplurality of data points or items 348. The items 348 represent thespecific data, images, answers to questions, etc. that are recommendedto be obtained for each category 344. In that regard, the categories 344may each be associated with a particular patient symptom or complaint.For example, Category 1 (block 350) may represent a particular patientsymptom such as lower back pain. In turn, the Data Collection Set 1(block 352) comprises a plurality of items, namely Item 1 (block 354),Item 2 (block 356), and so on to Item X (block 358). Each Item 1, 2, andX (354, 356, 358) represents a specific data point, image, answer to aquestion, or other information that is recognized as being beneficial indiagnosing the medical condition of a patient having lower back pain.The items in each category represent data or information that may beuseful in evaluating and diagnosing the patient. For example, in someembodiments where a patient complains of pain in a bony region, the datacollection set defined by the category includes an item that requiresobtaining an x-ray of the problem area. Similar correlations between thepatient's symptoms and the desired medical information and/or testsassociated with that symptom are defined for each of the categories ofthe data structure 342.

In some instances, some of the Items 1-X of the Data Collection Set 1(block 352) are optional. That is, some of the items included in theData Collection Set 1 (block 352) are not necessary for diagnosing thepatient, but may be beneficial in some instances. Similarly, some of theitems included in the Data Collection Set 1 (block 352) are necessaryfor a proper diagnoses of the patient and, therefore, should always beobtained. In some embodiments, the required and optional items arepredetermined and stored within a software application for eachcategory. In some embodiments, the treating physician or medicalpersonnel determines and/or modifies what items are required and/oroptional for a specific category. In some embodiments, the items areweighted by importance for each category. That is, items may not begiven a required or optional label, but rather will be rated based onthe relative importance and/or benefit of the item to the diagnosis ofthe patient. As shown, the data structure 342 includes a plurality ofcategories each with its own data collection set. For example, thecurrent data structure 342 includes Category 1 (block 350) and itscorresponding Data Collection Set 1 (block 352); Category 2 (block 360)and its corresponding Data Collection Set 2 (block 362); throughCategory Y (block 364) and its corresponding Data Collection Set Y(block 366).

Referring again to FIG. 21, step 326 also includes obtaining thediagnostic recommendations. For example, after grouping the patient intoone or more of the categories 344, the items 348 associated with each ofthe categories are obtained. Accordingly, the extent of the datacollection/diagnostic recommendations will vary depending on thecategorization of the patient. In some instances, at least some of thedata collection is provided by the patient's primary care physician orreferring physician. In that regard, the patient has often previouslyundergone testing and/or imaging that are included in the item lists ofthe categories. In some instances, this information is provided from theprior medical personnel to the current medical personnel over atelecommunications network, such as the internet, phone system, fax, orotherwise. In one particular embodiment, the data is stored in adatabase accessible by the current medical personnel. In addition to anydata that is available from previous medical personnel, the remainingitems that are suggested to be collected for each category are obtainedfrom the patient. These items may include standard information on thecurrent physical characteristics (e.g., height, weight, mobility, etc.)and condition of the patient. Further, the items may include goals as topost-treatment mobility, activity, or relative deformity of the patient.At least some of the items are obtained by determining the answers to aseries of diagnostic questions associated with a particular category. Inthat regard, the questions within each category may be nested such thatsubsequent questions depend on the answers to previous questions.Further, some or all of the items may be weighted so as to emphasize oneor more factors associated with a particular category. That is,particular items and the resultant information provided thereby aregiven more importance than diagnostic items.

The data collection sets include various types of items depending on thecategory. For example, in some embodiments imaging techniques areutilized to obtain additional data regarding the patient. In particular,in some embodiments radiographic images of the patient's anatomy areobtained. The radiographic images are then analyzed to identify therelevant data associated with a particular item. In some instances, theradiographic or other images comprise the item to be collected. In someembodiments, the patient's motion sequence and/or range of motion in oneor more anatomical areas is an item to be obtained in evaluating thepatient. Accordingly, in some embodiments the patient is put through aseries movements appropriate to determine the patient's motion sequenceand/or range of motion in the one or more anatomical areas. In otherembodiments, the items include obtaining patient images through the useof magnetic resonance imaging (“MRI”), computed tomography (“CT”), videofluoroscopy, and/or other imaging techniques. In general, the imagingobtains images of the patient's anatomy that are utilized to obtain datapoints or items as set forth in the data collection set for eachcategory associated with the patient.

After the relevant informational items have been determined andcollected at step 326, the method 320 continues at step 328 where thedata is analyzed. In some embodiments, the data is provided to one ormore software applications for analysis. In that regard, in someembodiments the answers to questions included in the item list forcategorizations are input directly into the relevant softwareapplication. With respect to the imaging data, in some embodiments thedata from the imaging is provided to one or more software applicationsin order to derive further information and/or new views of the imagingdata. Various brands or types of software for obtaining, analyzing, orotherwise handling patient data may be used for one or more of the datacategories. Also, multiple software applications may be applied to agiven set of item data. It is understood that data from each study canbe assembled together prior to submission to such software, or eachstudy can be treated individually. In some embodiments, these softwareapplications transform the raw images into mathematical or other formsthat can be utilized by other software applications and/or manipulatedvia a computer system and compared to other images and/or other datasets.

Generally, the software applications synthesize the information toidentify any abnormal medical conditions afflicting the patient. In someembodiments, the analysis of the data includes creating a 3-D and/or 2-Danimated model of the patient's anatomy. This model may be visuallyrepresented, such as on a computer screen or otherwise, in someembodiments. Generally, the animated model is substantially based on thedata obtained in step 306. In some embodiments, the animated model isused to highlight the problem areas and/or times in the patient'sanatomical motion sequence. In that regard, in some instances the modelincludes layers of anatomical features that are selectively included orremoved. For example, in one embodiment the patient's motion anatomy isgrouped into layers according to the various types of anatomical tissue,such as bones, cartilage, ligaments, tendons, muscles, and/orcombinations thereof. The animated model then analyzes motion accordingto each grouping of anatomical tissue and the interactions therebetween.

In some embodiments, the animated model combines diagnostic tests withthe imaging study. For example, in some embodiments the animated modelcombines muscle monitoring with the imaging study to identify musclecontractions and tensions during a motion sequence or protocol. Theresults of the muscle monitoring are combined with the other imagingdata to provide additional details and/or realism to the animated model.In other embodiments, the animated model utilizes center-of-balance orcenter-of-gravity data for the patient obtained during the motionsequence or protocol. Muscle monitoring and center-of-balance data aremerely examples of the types of additional data that may be combinedwith the imaging data in forming the animated model. Other types of thepatient data may also be utilized. In that regard, in some embodimentsthe treating physician or medical personnel selects the types of patientdata to be used in formulating the animated model.

The animated model includes additional features to allow medicalpersonnel and/or a computer system to analyze the patient. In thatregard, in some embodiments the animated model includes a stress gridoverlay that indicates potential areas of increased stress or strain onthe patient's anatomy, such increased muscle activity; overstretching ofmuscles, ligaments, and/or tendons; friction between bones; and/or otherareas of stress/strain. In some embodiments the model allows forzooming, panning, or otherwise changing the orientation of the view ofthe patient's anatomy. Users can adjust the orientation of the modelrelative to particular anatomical features to better observe or isolatea potential problem area. Similarly, the animated model allows a user topause, rewind, slow down, and/or speed up simulation of a motionsequence to better observe a potential problem. Further, the animatedmodel allows 3-D and/or 2D tracking of specific anatomical featuresthrough the motion sequences. In some embodiments, the softwareapplication that creates the animated model also highlights potentialproblem areas automatically based on a comparison to a standardizedmodel. In other embodiments, the treating physician or medical personnelnotes the potential problem areas based on their own observations. Insome embodiments, the problem areas are identified by the softwareapplication and/or medical personnel by recognizing an abnormal motionpattern(s). In some embodiments, the model is utilized internally by thesoftware application to identify the patient's potential medicalproblems, but no visual representation of the model is created.

After the data has been analyzed at step 328, the method 320 continueswith step 330 where the patient is classified based on the identifiedmedical conditions. In that regard, the patient is classified based onthe data/results provided in response to the items obtained in the datacollection sets. Generally, the patient is classified based oninformation that is important to the diagnosis and subsequent treatmentof the patient's medical condition. In some embodiments, the patient isclassified based on such things as the damaged anatomical features orareas, extent of limited range of motion, and/or other data related tothe patient's medical condition.

After the classification of the patient at step 330, the method 320continues with step 322 where a treatment plan is recommended for thepatient. In some instances, the patient's data is compared to a priorpatient data set for determining an appropriate treatment plan. In thatregard, there are multiple types of prior patient data sets that may beused. The particular prior patient data set utilized is determined bythe availability of the data sets and/or physician preference. In someembodiments, the multiple prior patient data sets are groupings within asingle larger data set. In other embodiments, the prior patient datasets are unrelated, individual data sets. Examples of the differenttypes of prior patient data sets include a particular physician's ownprior patients; an aggregated collection of patients from multiplephysicians, hospitals, and/or studies; patients from specific medicalpersonnel, such as a renowned physician, a mentor, a consultant, orother medical personnel; and/or a patient wizard using a probabilisticmatching system (i.e., grouping of patients with similar attributes tothe current patient). In some embodiments, the treating physician orother medical personnel at least partially defines or selects theparameters of the prior patient data set to be used. In someembodiments, the prior patient data set is a collection of priorpatients having similar medical conditions, medical histories, and/orpatient profiles to the current patient. The prior patient data setsinclude the selected treatment plans and relative success of those plansfor the prior patients. Accordingly, the current patient's physicalcharacteristics and attributes can be compared to prior patients withsimilar characteristics and attributes. Then, the one or more treatmentoptions that have been successful for prior patients withcharacteristics and attributes similar to the current patient may beidentified.

After a treatment plan has been recommended at step 332, the method 320continues with the selection of a appropriate treatment plan at step334. In some embodiments, the comparison of the patient analysis summaryand the prior patient data will identify a single treatment plan that isclearly considered best for the patient. In such instances, the singletreatment plan will be selected at step 334. However, in otherembodiments a plurality of treatment plans are identified by thecomparison as possible treatment plans for the current patient. In suchembodiments, the plurality of treatment options are sorted and/orscreened to further narrow the treatment options. In some instances, thetreatment options are screened based on physician preference. Forexample, if a physician prefers a particular surgical procedures orapproach, then the available treatment options are limited to those thatutilize the preferred procedure or approach. As another example, thetreatment options are sorted or ranked based on the likelihood ofsuccess of the treatment plan based on the data for previous patientshaving a similar profile to the current patient. Similarly, in someembodiments the treatment options are sorted or ranked based on theprevious procedures performed by the treating physician/surgeon and therelative success of those procedures. Based on the comparisons to priorpatient data, physician preferences, and likelihood of success aspecific treatment plan is selected for the patient.

After selecting a treatment plan at step 334, the method 320 continuesat step 336 by discussing and/or educating the patient about theselected treatment option. In that regard, the results of the analysesand modeling (if performed) are shown and/or explained to the patient tosupport the decision to go with a particular treatment plan. Further, inthe case of a treatment plan that includes inserting an implant orotherwise employing a medical device, the patient may be given access toadditional product information regarding the medical device. In someembodiments, discussing the treatment option with the patient isaccomplished over the internet, an intranet, computer network,telecommunications network, or other type of remote connection. In thatregard, the link between the patient and the medical professional may bea secure link or secured communication channel so as to protect thepatient's confidentiality. In some instances, the treatment options areprovided over a secure website. The patient is provided access to thesecure website via a username and password associated with the patient.In addition to providing the patient information regarding the selectedtreatment option(s), the patient interface also provides the patientwith the ability to ask questions. In some embodiments, the interfaceincludes a query box that is filled out and submitted by the patient,which a medical professional replies to. In other embodiments, theinterface is in the form of a chat or instant messaging session. Thepatient may ask questions over the chat session and the medicalpersonnel can provide answers to these questions immediately or seekanswers to the questions and reply to the patient at a later time. Inyet other embodiments, the patient interface may be combined withvideo-conferencing or telephonic-conferencing to provide additionalinformation and opportunities for questions to the patient.

After selection of the treatment option at step 334 and education of thepatient at step 336, the method 320 continues with step 338 in selectedtreatment plan is executed. The treatment plan is executed in accordancewith the planning that has occurred in the previous steps or as part ofstep 338. In that regard, in some instances of a surgical procedure theprocedure is monitored intra-operatively to ensure compliance with theplanned procedure. The actual surgical procedure, as monitored, iscompared in real-time, or approximately real-time, to the plannedtreatment. Thus, the actual placement of an implant and/or fixationdevices is compared to the intended placement and/or associated errorfields as defined in the treatment plan. In this manner, an analysis ofthe placement of the surgical components is performed before the patientleaves the operating room. In that regard, the actual surgical procedureis modified as needed to ensure that it coincides with the error fieldsof the planned treatment. Any adjustments that need to be made to complywith the treatment plan can be accomplished without the need for arevision surgery or a return to the operating room.

After executing the treatment plan or at least a part thereof at step338, the method 320 continues with step 340 in which a post-treatment,follow-up analysis is performed. In some embodiments, the post-treatmentanalysis is substantially similar to steps 324, 326, and/or 328described above. In some embodiments, the post-treatment analysis step340 includes comparing the predicted results of the treatment plan tothe actual results of the treatment. Any discrepancies are identifiedand utilized to improve the correlation between the predicted resultsand the actual results of the treatment plan, as indicated by thefeedback loop of step 341. In that regard, in some embodiments theparameters utilized for creating the models are updated and modifiedbased on the identified discrepancies. Ideally, the predicted resultsare substantially similar to the actual results of the treatment plan.In some embodiments, the post-treatment analysis is performed at setintervals after the initial treatment. In one particular embodiment, thepatient goes through post-treatment analysis at least at 2 weeks, 6weeks, and 3 month intervals after the initial treatment.

By monitoring the resultant data from each patient for each treatmentplan, a statistical correlation between medical conditions and treatmentoptions is established. This statistical correlation is utilized inselecting the treatment plans for subsequent patients. The currentpatient's resultant data is routed and stored as a part of a studyand/or other collection of data into a database for future access.Generally, the data will be de-identified from the particular patient,so as to preserve confidentiality and impartially of the data and tocomply with applicable privacy laws. For example, the patient's name,social security number, address, and/or other sensitive information areremoved from the data, while the patient's physical characteristics,selected treatment plan, and outcome are maintained. In someembodiments, the data is entered into the database(s) by a medicalprofessional as part of the post-treatment analysis. The data related tocurrent patient's outcome creates a feedback loop that providesconfirmation of prior information and/or new information from which themedical professionals can modify the treatment plans and/or medicaldevice manufacturers can modify the implants or devices.

In some instances, the patient data, images, models, simulations, and/orother information of the present disclosure are processed, compiled, orotherwise manipulated. In that regard, the methods, systems, andconcepts described in the following references are utilized inconnection with the patient data, images, models, simulations, and/orother information in some instances: U.S. Pat. No. 5,970,499 filed Apr.11, 1997 and titled “Method and Apparatus for Producing and AccessingComposite Data”; U.S. Pat. No. 6,009,212 filed Jul. 10, 1996 and titled“Method and Apparatus for Image Registration”; U.S. Pat. No. 6,226,418filed Nov. 5, 1998 and titled “Rapid Convolution Based Large DeformationImage Matching Via Landmark and Volume Imagery”; U.S. Pat. No. 6,253,210filed Aug. 25, 1999 and titled “Method and Apparatus for Producing andAccessing Composite Data”; U.S. Pat. No. 6,408,107 filed Nov. 14, 2000and titled “Rapid Convolution Based Large Deformation Image Matching ViaLandmark and Volume Imagery”; U.S. Pat. No. 6,526,415 filed Jun. 11,2001 and titled “Method and Apparatus for Producing an AccessingComposite Data”; U.S. Pat. No. 6,553,152 filed Apr. 27, 1999 and titled“Method and Apparatus for Image Registration”; U.S. Pat. No. 6,611,630filed Jun. 7, 1999 and titled “Method and Apparatus for Automatic ShapeCharacterization”; U.S. Pat. No. 6,633,686 filed Sep. 20, 2000 andtitled “Method and Apparatus for Image Registration Using LargeDeformation Diffeomorphisms on a Sphere”; U.S. Pat. No. 6,694,057 filedJan. 27, 2000 and titled “Method and Apparatus for Processing Imageswith Curves”; U.S. Pat. No. 6,708,184 filed May 4, 2001 and titled“Method and Apparatus for Producing and Accessing Composite Data Using aDevice Having a Distributed Communication Controller Interface”; U.S.Pat. No. 6,754,374 filed Dec. 16, 1999 and titled “Method and Apparatusfor Processing Images with Regions Representing Target Objects”; each ofwhich is hereby incorporated by reference in its entirety.

Referring now to FIG. 23, shown therein a method 400 for visualizing andanalyzing anatomical motion according to one embodiment of the presentdisclosure. Generally, the method 400 utilizes sensors, wirelesstelemetry or other communication means, and 3-D or 2-D reconstructionsof the anatomy to visualize and analyze the anatomical motion. Asdescribed in greater detail below, the method 400 is for use in patienttreatment. For example, in various embodiments the method 400 is usedfor diagnosing and/or categorizing a patient's medical problems,creating a patient treatment plan (e.g., surgical procedures, physicaltherapy, chemical therapy (e.g., pharmaceuticals or other drugtherapies), and combinations thereof), monitoring the progress of apatient treatment plan, comparing the effectiveness of differenttreatment plans for patients with similar medical problems, and numerousother medical applications. Further, the method 400 is particularly wellsuited for use in orthopedic applications. For example, in oneparticular embodiment the method 400 is used in the analysis andtreatment of spinal disorders. As another example, the method 400 isalso used in the analysis and treatment of patients likely to receiveprosthetic joint replacements (e.g., hip, knee, vertebrae, and ankle) inother embodiments. In such embodiments, the method 400 is configured toprovide information useful in determining the appropriate prostheticimplant for a patient (e.g., shape, size, design, material, etc.) and isfurther configured to monitor the effectiveness of the prosthetic afterimplantation in some instances.

The method 400 begins at step 402 in which one or more sensors areintroduced. In some embodiments, the sensors are accelerometer and/orgyroscopes. In particular, in some embodiments the sensors comprise amicro-accelerometer. In some aspects, the micro-accelerometer is eitherMEMS -based or piezoelectric-based. MEMS-based micro-accelerometers arepreferred in some instances because there is no need for motion toobtain useable data. Generally, the sensors are placed in closeproximity to an anatomical structure of interest. In this manner, thesensors are utilized to correlate the position of the anatomicalstructure based on the motion data obtained from the sensor. In someinstances, a plurality of sensors may be utilized adjacent to a singleanatomical feature to provide more accurate position data for theanatomical structure and/or provide redundancy. In some instances,position information is extrapolated using secondary systems in thesensor device. For example, in some instances a wireless communicationsinterface used for sending data between the sensors and a processingunit can be used to detect the relative distances between the sensorsand the processing unit through ping-response time measurements. Thesensors may be implanted into the patient's body adjacent to theanatomical feature of interest, placed on the skin of the patientadjacent to the anatomical feature(s) of interest, and/or placed onclothing of the patient adjacent to the anatomical feature(s). In someembodiments, implantable sensors are preferred. In some instances,sensors are used both inside and outside of the patient's body.Implantable sensors facilitate direct contact with the anatomicalfeature(s) of interest or at least provide substantially closerplacement to the anatomical features than sensors that remain outsidethe patient's body. In that regard, implantable sensors facilitate theaccurate detection of the position of internal anatomical features thatcannot be accurately determined with external sensors alone.

In some embodiments, the implantable sensors are configured forengagement with bone. In that regard, the implantable sensors are partof a bone screw or other bone fixation device in some embodiments. Inother embodiments, the implantable sensors are secured to the bone via abiocompatible adhesive, a structural fixation device (screw, staple,etc.), combinations thereof, and/or other otherwise secured to the bone.Generally, engaging the implantable sensors with bone provides a fixedorientation between the sensor and the bone, which allows a goodcorrelation between the position of the sensor and the position of thebone. In other embodiments, the sensors are configured for engagementwith softer tissues. In such embodiments, the sensors include featuresto prevent unwanted movement of the sensors relative to the tissue.Where the sensors are implanted inside the body, the sensors areintroduced via a guidewire, needle, catheter, tube, and/or othersuitable implantation means. Preferably, the sensors are implanted usinga minimally invasive procedure and in some instances are implantedpercutaneously. In some embodiments, systems and methods may be used asdescribed in U.S. patent application Ser. No. 10/985,108 filed Nov. 10,2004 and titled “Method and Apparatus for Expert System to Track andManipulate Patients,” herein incorporated by reference in its entirety.

The sensors are utilized for tracking the position of one or moreanatomical features. In that regard, one or more sensors are placedadjacent each anatomical feature of interest. In some embodiments thesensors are configured for identifying the location of one or more ofthe following anatomical features or parts thereof: heels, ankles,knees, hips, iliac crests, sacrum, pelvis, spinal column, spinal columnregions, vertebrae, transverse processes, spinal processes, clavicles,and other anatomical features. In one particular embodiment, the sensorsare placed on a plurality of vertebrae. As will be described in greaterdetail below, the relative motion of the sensors placed on each of theplurality of vertebrae are utilized to obtain relative orientation andmotion information for the vertebrae. The actual anatomical features forwhich sensors are located adjacent to depends on numerous factorsincluding physician preference, patient condition, treatment plans,surgical procedures, and other factors. In some embodiments, theanatomical feature(s) of interest may be selected by the treatingphysician or technician.

After the sensors have been introduced at step 402, the method 400continues at step 404 in which an imaging protocol is performed. Inorthopedic applications, the imaging focuses on the relevant skeletalstructures of the patient. Generally speaking, the imaging of step 404may include x-ray, fluoroscopy, and/or CT scans. X-ray machines may beutilized to obtain snap-shot images of the patient's skeletal structure.Fluoroscopy machines may be utilized to obtain real-time images of thepatient's skeletal structure. In some embodiments, the imaging step 404is utilized to obtain images of the patient's spinal column, pelvis,iliac crest, sacrum, hips, shoulders, clavicles, skull, arms, legs,knees, ankles, feet, and/or combinations thereof. In some embodiments,the imaging protocol is utilized to obtain at least sagittal and frontalimages of the patient's anatomy. In some embodiments, the patient simplyturns to obtain the desired perspective view for the radiograph. In thatregard, the patient may be asked to physically turn herself or himselfor, in some embodiments, a moveable platform rotates the patient betweenthe desired positions such that the patient can remain substantiallystationary between positions. In some embodiments the imaging step 404simultaneously obtains the sagittal and frontal images of the patient'sanatomy. In addition to the sagittal and frontal views, the other viewsof the patient's anatomy that would be advantageous to patient analysisare obtained.

After imaging protocol has been performed at step 404, the method 400continues at step 406 in which a model of the patient's relevantanatomical features is created. Generally, the data from the imagingprotocol is utilized to create the model. In one particular embodiment,the data from the imaging protocol is utilized to segment the model intothe individual bones of the patient. In that regard, a joint is modeledby the combination of individual bones that come together to form thejoint. In some embodiments, the dimensions of the implanted sensor areknown and utilized to correlate bone position to the sensor position.Further, the orientation of the sensor to the bone is established by anasymmetry in the structure of the sensor that is identifiable throughthe imaging protocol. Accordingly, in some embodiments the knowndimensions and features of the implanted sensors are utilized increating the model of the patient's anatomical features. The model iseither a 3-D or 2-D representation of the patient's anatomy. In someembodiments, the model is animated to illustrate a motion sequence ofthe patient's anatomy. The animated model is particular beneficial inthe diagnosis and treatment of orthopedic joints. One particular methodfor modeling the patient's anatomy is to provide or develop a highlyaccurate model of a generic skeleton, and then map a model of thespecific patient derived from an imaging study to the generic skeleton.In some instances this is accomplished through identifying key landmarkson each bone, and then growing or shrinking the original master modelaccording to the measured distances of these landmarks on the patient.Through this method, a useful 3D model of a patient is created that canthen undergo kinematics and/or finite element analysis. In someinstances, the modeling is performed in a manner similar to thatdescribed by Rajamani, K. T.; Joshi, S. C.; Styner, M. A., “Bone modelmorphing for enhanced surgical visualization,” Biomedical Imaging: Nanoto Macro, 2004. IEEE International Symposium on, vol., no., pp.1255-1258 Vol. 2, 15-18 Apr. 2004, hereby incorporated by reference inits entirety.

After creation of the model at step 406, the method 400 continues atstep 408 with the performance of a diagnostic protocol. Generally, thediagnostic protocol is performed to measure joint motion and/or relativemotion between anatomical features. In a first aspect, the diagnosticprotocol utilizes the relative motion between the implanted sensors tomonitor joint motion. That is, the movement of each sensor with respectto the other sensors is tracked and utilized to determine the relativemotion between the anatomical features associated with each sensor. In asecond aspect, the diagnostic protocol utilizes the absolute positionsof the sensors to correlate to the motion of the anatomical features.That is, the positions of the sensors are tracked with respect to areference point (e.g., a signal receiver), which can in turn be utilizedto determine the motion of the anatomical features. In some embodiments,the positions of the sensors are monitored using wireless telemetry tomeasure the distances between each sensor. For example, in someinstances each sensor is registered with a signal receiver and theposition of the sensor is tracked using wireless telemetry. Based on thecommunication of the sensor with the signal receiver a time of flightcalculation can be made to triangulate the position of the sensor withrespect to the signal receiver over time. The positions of each of thesensors can then be compiled to identify the relative motion sequence ofthe anatomical features with respect to one another. Taken together, themotion of the anatomical features with respect to one another define thejoint motion.

In either case, the relative orientations of the sensors are initiallydetermined at a static point or a reference point. In some instances,the relative orientations of the sensors at the static point aredetermined by the direction of gravity as measured by each of theaccelerometer sensors. In other instances, the relative orientations ofthe sensors are determined by the positions of the sensors obtained fromthe telemetry communication of the sensors with the signal receiver.Once the relative orientations and/or positions of the sensors have beendetermined, the patient is moved through a diagnostic protocolcomprising a series of movements. During the series of movements theacceleration and/or positional data from the sensors is obtained. Theactual series of movements the patient is put through depends upon thespecific medical condition of the patient. In some embodiments, thediagnostic protocol comprises having the patient walk on a treadmill. Insome instances, a reference point or time=0 point is established. Inthat regard, the reference point is a starting point for identifying themotion sequence of the patient's anatomical features. Accordingly, insome embodiments the reference point is established based on an imageobtained during the imaging step 404.

In some embodiments, an electromagnetic measurement system is utilizedto track the positions of the implantable sensors. For example, theelectromagnetic measurement system can detect the presence of sensorsexcitable by an electromagnetic field to determine the position of theanatomical features associated with the sensor. As described above, thesensors may be external or implantable. The electromagnetic measurementsystem may utilize a computer system to calculate the 3-D position ofthe anatomical feature(s) based on the position of the sensors. In someembodiments, the electromagnetic measurement system is configured todetect the position of sensors in a fixed volume of space. In thatregard, in some embodiments the fixed volume of the electromagneticmeasurement system is sufficient to obtain the position of all relevantanatomical features of a patient. In other embodiments, however, thefixed volume may be sufficient to obtain 3-D positions of only someanatomical features of a patient. Where the fixed volume is sufficientto obtain 3-D positions of some, but not all of the patient's anatomicalfeatures, a portion of the electromagnetic measure system (e.g., theelectromagnetic field generator) may be moveable such that the 3-Dpositions of the anatomical features of most interest can be obtained.In lieu of or in addition to the electromagnetic measurement system, aninfrared system and/or a video system are utilized for determining the3-D position of the sensors in some embodiments. The video system may bea single camera or multi-camera system. In that regard, a multi-cameravideo system may take the resulting video triangulate the positions ofanatomical features of interest using a computer system. Video system inthis context is understood to include still photography in addition tomoving video.

After the diagnostic protocol of step 408 has been performed, the method400 continues at step 410 in which the model of step 406 is updatedand/or a new 3-D and/or 2-D animated model of the patient's anatomy iscreated to visualize the patient's anatomy. Generally, the animatedmodel is based on the data obtained from the imaging of step 404 and thediagnostic protocol of step 408. In some embodiments, the animated modelis used to highlight the problem areas and/or times in the patient'sanatomical motion sequence. In that regard, the model includes layers ofanatomical features that are selectively included or removed. Forexample, in one embodiment the patient's motion anatomy is grouped intolayers according to types of anatomical tissue, such as bones,cartilage, ligaments, tendons, muscles, and/or combinations thereof. Theanimated model then analyzes motion according to each grouping ofanatomical tissue and the interactions therebetween.

In some embodiments, the animated model combines diagnostic tests withthe imaging study. For example, in some embodiments the animated modelcombines muscle monitoring with the imaging study to identify musclecontractions and tensions during a motion sequence or protocol. Themuscle monitoring is accomplished through the use of additional sensorsin some embodiments. In other embodiments, the muscle monitoring isaccomplished through the use of external sensing systems. The results ofthe muscle monitoring are combined with the other imaging data toprovide additional details and/or realism to the animated model. Inother embodiments, the animated model utilizes center-of-balance orcenter-of-gravity data for the patient obtained during the motionsequence or diagnostic protocol. Muscle monitoring and center-of-balancedata are merely examples of the types of additional data that may becombined with the imaging data in forming the animated model. Othertypes of the patient data may also be utilized. In that regard, in someembodiments the treating physician or medical personnel selects thetypes of patient data to be used in formulating the animated model.

The method 400 continues with step 412 in which the data obtained fromthe diagnostic protocol of step 408 is analyzed. In some embodiments,the animated model includes features to allow medical personnel and/or acomputer system to analyze the patient's motion sequence. In thatregard, in some embodiments the animated model includes a stress gridoverlay that indicates potential areas of increased stress or strain onthe patient's anatomy, such increased muscle activity; overstretching ofmuscles, ligaments, and/or tendons; friction between bones; and/or otherareas of stress/strain. In some embodiments the model allows forzooming, panning, or otherwise changing the orientation of the view ofthe patient's anatomy. A user adjusts the orientation to better observeor isolate a potential problem area. Similarly, the animated modelallows a user to pause, rewind, slow down, and/or speed up simulation ofa motion sequence to better observe a potential problem. Further, theanimated model allows 3-D and/or 2D tracking of specific anatomicalfeatures through the motion sequences. In some embodiments, the animatedmodel highlights potential problem areas automatically based on acomparison to a standardized model. For example, the system may identifyanatomical features with a motion sequence outside of a predeterminedrange. In that regard, the standardized model and/or predetermined rangeof normal motion are at least partially defined by a general patientpopulation. In some embodiments, the treating physician or medicalpersonnel highlights potential problem areas based on their observationsof the patient's motion sequence. In some embodiments, the problem areasare identified by a computer system and/or medical personnel byrecognizing an abnormal motion pattern(s). In some instances, theabnormal motion patterns are grouped into motion signatures that areindicative of a medical condition. Each of the motion signatures, inturn, are associated with appropriate medical treatment options forcorrecting the medical condition(s) associated with the motionsignature. The method 400 concludes at step 414 by summarizing theresults of the analysis of step 412.

Referring now to FIG. 24, shown therein a method 420 for usingimplantable sensors in an image-guided treatment according to oneembodiment of the present disclosure. Generally, the method 420 utilizesimplantable sensors as fiducial markers for use during the image-guidedprocedure. In this context a fiducial marker provides a reference pointfor orientation of implants and surgical instruments during theimage-guided treatment. In some instances, the sensors are configured tobe affixed to portions of a patient's body, especially the bony anatomy,and are configured to show up in an x-ray or other imaging so thatsucceeding scans or pictures may be registered or correlated to oneanother. In accordance with the present disclosure, the implantablesensors are capable of being mapped in three-dimensional format relativeto one another as described above. Generally, the relative motion of thesensors between one another may be utilized to track the motion of theanatomical features of the patient and/or the positions of the sensorsrelative to a reference point or receiver may be utilized to track themotion of the anatomical features. The method 420 is particularly wellsuited for use in orthopedic surgical procedures, such as spinalsurgeries, joint replacements, and other orthopedic procedures. In thatregard, the method 420 is configured to provide positional informationuseful in ensuring the appropriate placement and orientation of anyimplants and/or fixation devices during the surgical procedure. Further,the implantable sensors are used to monitor the placement andorientations of the implant and/or fixation devices after implantationin some instances.

The method 420 begins at step 422 in which one or more sensors areintroduced. In some embodiments, the sensors are accelerometer and/orgyroscopes. In particular, in some embodiments the sensors comprise amicro-accelerometer. Generally, the sensors are placed in closeproximity to an anatomical structure of interest. In this manner, thesensors are utilized to correlate the position of the anatomicalstructure based on the position of the sensor. In some instances, aplurality of sensors may be utilized adjacent to a single anatomicalfeature to provide more accurate position data for the anatomicalstructure. The sensors may be implanted into the patient's body adjacentto the anatomical feature of interest and/or placed on the skin of thepatient adjacent to the anatomical feature(s) of interest. For mostprocedures, implantable sensors are preferred. In some instances,sensors are used both inside and outside of the patient's body.Implantable sensors facilitate direct contact with the anatomicalfeature(s) of interest or at least provide substantially closerplacement to the anatomical features than sensors that remain outsidethe patient's body. In that regard, implantable sensors facilitate theaccurate detection of the position of internal anatomical features thatcannot be accurately determined with external sensors alone.

In some embodiments, the implantable sensors are configured forengagement with bone. In that regard, the sensors may be secured to thesurface of a bone (e.g., using an epoxy or other biocompatibleadhesive), inserted into a void in the bone, press-fit into the bone,cemented into the bone, and/or imbedded in a housing or device that issecured to the bone. In some embodiments, the implantable sensors arepart of a bone screw or other bone fixation device. For example,referring more particularly to FIGS. 25 and 26, shown therein is a bonescrew 430 in accordance with one embodiment of the present disclosure.The bone screw 430 comprises a head portion 432 and a body portion 434.In the current embodiment, the body portion 434 is threaded such that itmay be screwed into a bone of a patient. In other embodiments, the bonescrew is secured to the bone via a biocompatible adhesive, surfacecoatings or treatments (e.g. chemical etching, bead-blasting, sanding,grinding, serrating, diamond-cutting, coating with a biocompatible andosteoconductive material (such as hydroxyapatite (HA), tricalciumphosphate (TCP), or calcium carbonate), or coating with osteoinductivematerials (such as proteins from the transforming growth factor (TGF)beta superfamily or bone-morphogenic proteins, such as BMP2 or BMP7)),other structural fixation devices (e.g., staple, nail, etc.), and/orcombinations thereof. Further, in some instances the bone screwincorporates one or more biologic materials. As shown, the body portion434 also contains a sensor housing 436 therein. The sensor housing 436contains all of the electronics and associated elements of the sensor.Depending on the type of sensor utilized the housing 436 containdifferent elements. While the sensor housing 436 is shown as beingpositioned substantially centrally within the body portion 436, in otherembodiments the sensor is positioned off-center, adjacent an end orsurface of the body, and/or within the head portion of the bone screw430. The illustrated position of the housing 436 is for exemplarypurposes only and should not be considered limiting.

The bone screw 430 can be utilized to create a model of the patient'sanatomy. In that regard, in some embodiments the bone screw 430 isidentified in imaging studies in relation to anatomical features of thepatient. In some embodiments, the size of the bone screw 430 is welldefined such that the relative size of the bone screw to the anatomicalfeatures is utilized in creating the model of the anatomical features.To that end, the bone screw 430 has a length 438 extending between aproximal end 440 and a distal end 442. Further, the head portion 432 ofthe bone screw has a height or thickness 444 extending between itsuppermost portion and its lowermost portion. The body portion 434 of thebone screw 430 has a height or thickness 446 as measured from the outerportion of the bone screw threads. In other embodiments, such as a nailembodiment, the body portion 434 has a substantially constant height446. In the current embodiment, the height 444 of the head portion 432is larger than the height 446 of the body portion 434. In otherembodiments, the height of the head portion is substantially equal to orless than the height of the body portion.

The head portion 432 is configured for engagement with a driving toolsuch that the driving tool may be utilized to secure the bone screw 430into a bone. In the current embodiment, a majority of the head portion432 is configured for engagement with a hex-shaped driver. Accordingly,the bone screw 430 may be secured into the bone by rotatingly drivingthe body portion 434 into the bone with a hex-shaped driver (not shown).As best seen in FIG. 26, the head portion 432 also includes a portion448 that provides the bone screw 430 with an asymmetric profile. Thatis, the portion 448 provides the bone screw 430 with a distinguishingfeature such that the orientation of the bone screw can be determinedwhen viewed in a image. Accordingly, the bone screw is not asymmetric inall embodiments. Rather, in some embodiments the bone screw comprises asubstantially symmetrical profile, but includes one or more featuresthat allow the orientation of the bone screw to be determined. Referringto FIG. 27, shown therein is a bone screw 449 according to anotheraspect of the present invention. The bone screw 449 is substantiallysimilar to bone screw 430 in many respects, however, the bone screw 449includes a head portion 450 illustrating an alternate profile. Inparticular, the head portion 450 includes a majority portion 451 havinga substantially circular profile and a minority portion 452 having asubstantially planar profile. Accordingly, the orientation of the headportion 450 and, in turn, the bone screw 449 is determined by theposition of the minority portion 452 relative to the majority portion451.

Generally, engaging the implantable sensors with bone provides a fixedorientation between the sensor and the bone, which allows a goodcorrelation between the position of the sensor and the position of thebone. In other embodiments, the sensors are configured for engagementwith softer tissues. In such embodiments, the sensors include featuresto prevent unwanted movement of the sensors relative to the tissue.Where the sensors are implanted—temporarily or permanently—inside thebody, the sensors are introduced via a guidewire, needle, catheter,tube, and/or other suitable implantation means. Preferably, the sensorsare implanted using a minimally invasive procedure and in some instancesare implanted percutaneously.

One or more sensors are placed adjacent to or within each anatomicalfeature of interest. In some embodiments the sensors are positionedadjacent to one or more of the following anatomical features or partsthereof: heels, ankles, knees, hips, iliac crests, sacrum, pelvis,spinal column, spinal column regions, vertebrae, transverse processes,spinal processes, clavicles, and other anatomical features. In oneparticular embodiment, the sensors are placed on a portion of aplurality of vertebrae. In one specific embodiment, the sensors areplaced on the spinous processes of at least two adjacent vertebrae. Theactual anatomical features for which sensors are located adjacent todepends on numerous factors including physician preference, patientcondition, treatment plans, surgical procedures, and other factors. Insome embodiments, the anatomical feature(s) of interest are selected bythe treating physician or technician.

After the sensors have been introduced at step 422, the method 420continues at step 424 in which an imaging technique is utilized toobtain an image of the patient with the sensors attached to thepertinent anatomical features. In orthopedic applications, the imagingfocuses on the relevant skeletal structures of the patient to which thebone screw 430 or other sensor has been attached. Generally speaking,the imaging of step 424 may include x-ray, fluoroscopy, and/or CT scans.X-ray machines may be utilized to obtain snap-shot images of thepatient's skeletal structure. Fluoroscopy machines may be utilized toobtain real-time images of the patient's skeletal structure. In someembodiments, the imaging step 424 is utilized to obtain images of thepatient's spinal column, pelvis, iliac crest, sacrum, hips, shoulders,clavicles, skull, arms, legs, knees, ankles, feet, and/or combinationsthereof. In some embodiments, the imaging technique is utilized toobtain at least sagittal and frontal images of the patient's anatomy. Inaddition to the sagittal and frontal views, other views of the patient'sanatomy that are advantageous to patient analysis are obtained in someinstances.

The method 420 also includes step 426 in which the relative positions ofthe sensors is determined. In that regard, the relative positions of thesensors are determined with respect to the anatomical features of thepatient and/or the other sensors. Generally, the implanted sensorsinclude features that allow them to be visualized on the images obtainedusing the imaging technique. In some instances, the sensors or thehousing of the sensors (such as bone screw 430) are substantiallyradiopaque so as to be visible on x-ray and/or fluoroscopy imaging. Inthat regard, in some embodiments the sensors or the housing of thesensors (such as bone screw 430) include features, such as asymmetricprofiles or otherwise, that allow the orientation of the sensor/housingrelative to the anatomical features to be determined from the images.

From the images a 3-D or 2-D model of the patient's anatomy can becreated. In some embodiments, the model is animated to illustrate and/ortrack a motion sequence of the patient's anatomy. In some embodiments,the model can be updated in approximately real-time based on theposition of the sensors and/or accelerometer information provided by thesensors to provide the surgeon or other medical personnel with therelative locations of the anatomical features with respect to oneanother. In some embodiments, the model utilizes the relative motionbetween the implanted sensors to monitor and update anatomicalpositioning. The movement of each sensor with respect to the othersensors is tracked and utilized to determine the relative motion betweenthe anatomical features associated with each sensor. The relativepositions of the anatomical features is determined therefrom. In someembodiments, the model utilizes the absolute positions of the sensors tocorrelate to the position of the anatomical features. That is, thepositions of the sensors are tracked with respect to one or morereference points (e.g., a signal receivers), which can in turn beutilized to determine the position of the anatomical features. In someembodiments, the positions of the sensors are monitored using wirelesstelemetry to measure the distances between each sensor. For example, insome instances each sensor is registered with the one or more signalreceivers and the position of the sensor is tracked using wirelesstelemetry. Based on the communication of the sensor with the signalreceiver a time of flight calculation can be made to triangulate theposition of the sensor with respect to the signal receiver.Triangulation can be done either by lateration (i.e., determiningdistance measurements to the sensors from the receivers) or byangulation (i.e., determining angles between the sensors and thereceivers and computing the location of the sensors based on the fixeddimensions between the receivers).

The method 420 continues with step 428 in which a treatment is performedutilizing positional data provided by the sensors. In that regard, adetailed treatment plan may have been established and modeled asdescribed above with respect to other embodiments. Accordingly, theimplanted sensors and resulting data may be utilized in step 428 toensure compliance with the planned treatment and/or ensure that thetreatment is performed within a predetermined error field.

Referring more particularly to FIG. 28, shown therein is a system 460illustrating step 428 of method 420 according to one particularembodiment of the present disclosure. In that regard, the system 460shows an upper vertebra 462, a lower vertebra 464, and an intervertebraldisc space 466. A bone screw 430, including sensor 436 therein, has beensecured to each of the upper and lower vertebrae 462, 464. In theillustrated embodiment, the natural disc has been removed such that theintervertebral disc space 466 and the vertebrae 462, 464 are configuredto receive an artificial disc prosthesis 468. In the illustratedembodiment, the prosthesis 468 comprises an upper portion 470 configuredfor engaging with the upper vertebra 462 and a lower portion 472configured for engaging with the lower vertebra 464. The upper portion470 articulatingly engages the lower portion 472. Each of the upper andlower portions 470, 472 include sensors 474 therein. In the illustratedembodiment, the sensors are positioned adjacent the anterior andposterior portions of the prosthesis 468. These positions are forexemplary purposes only and should not be considered limiting. Inparticular, it is contemplated that one or more sensors 474 may bepositioned within and/or attached to the disc prosthesis 468. The one ormore sensors 474 are utilized to track the placement of the discprosthesis within the intervertebral disc space as it is implanted and,in some embodiments, after implantation. In that regard, the position ofthe sensors 474 are compared to the positions of the sensors 436 todetermine the relative position of the prosthesis 468 within the discspace 466 in some embodiments. In that regard, in some embodiments thesensors 474 communicate directly with the sensors 436 to determinerelative position of the prosthesis 468. In other embodiments, acentralized receiver or imaging device determines the position of thesensors 436 and 474 to determine the relative position of the prosthesis468. Where the prosthesis 468 includes one or more sensors 436 the toolutilized for inserting the prosthesis need not necessarily have sensorsbecause the position of the prosthesis can be determined from thesensors therein. However, in the illustrated embodiment the system 460includes an insertion tool 476 having a plurality of sensors 478.

Similar to the sensors 474 within the prosthesis 468, the sensors 478 ofthe insertion tool 476 are utilized to track the placement of the discprosthesis within the intervertebral disc space 466 as it is implanted.To ensure proper orientation between the insertion tool 476 and theprosthesis 468 to allow a correlation between the position of the tooland the position of the prosthesis, the prosthesis 468 includesapertures (not shown) for receiving an engagement portion of theinsertion tool in some embodiments. The position of the sensors 478 arecompared to the positions of the sensors 436 to determine the relativeposition of the prosthesis 468 within the disc space 466 in someembodiments. In that regard, in some embodiments the sensors 478communicate directly with the sensors 436 to determine relative positionof the prosthesis 468. In other embodiments, a centralized receiver orimaging device determines the position of the sensors 436 and 478 todetermine the relative position of the prosthesis 468. In someembodiments, both sets of sensors 474 and 478 within the prosthesis andthe insertion tool 476 are utilized to monitor positioning of theprosthesis 468.

In some embodiments, the model of the patient's anatomical features isupdated in approximately real-time to illustrate the position of theprosthesis 468 and/or insertion tool 476 relative to the vertebrae 462,464 and the sensors 436. In some embodiments, an image guided surgerysystem utilizes the positional data from the sensors 436, 474, and/or478 to ensure proper placement and orientation of the prosthesis withinthe disc space 466. In that regard, in some embodiments the insertiontool 476 is part of the image guided surgery system. Further, in someembodiments the image guided surgery system is communication with themodel and/or the model is a component of the image guided surgery systemsuch that a visualization of the prosthesis and associated anatomicalfeatures of the patient is provided to confirm proper placement of theprosthesis within the disc space. In some embodiments, the image guidedsurgery system utilizes the model in monitoring the placement of theprosthesis. It is understood that the procedure described above isexemplary and that numerous other treatment procedures may be performedusing the concepts described.

Referring now to FIG. 29, shown therein is a method 500 for selectingand modifying implant parameters using implanted sensors according toone embodiment of the present disclosure. The method 500 begins withstep 502 in which one or more sensors are introduced. In that regard,the particular type of sensors that are introduced will depend on thepatient anatomy to be monitored. In some embodiments, the pertinentanatomical features of the patient comprise a joint. In suchembodiments, accelerometers and/or gyroscopes are utilized as thesensors. Use of the accelerometers and/or gyroscopes allows the sensorsto track the motion of the joint and thereby monitor the performance ofthe joint and any implants or other medical treatments associatedtherewith. Generally, the sensors are placed in close proximity to ananatomical structure of interest. In that regard, in some instances thesensors are placed on an implant, prosthesis, fixation device, or otherdevice that is part of a treatment plan. In other instances, the sensorsare stand alone units placed adjacent to the anatomical structure ofinterest and any associated devices if present.

In this manner, the sensors are utilized to correlate the position ofthe anatomical structure based on the position of the sensor. In someinstances, a plurality of sensors are utilized adjacent to a singleanatomical feature to provide more accurate position data for theanatomical structure. Depending on the anatomical features of interest,the sensors may be implanted into the patient's body adjacent to theanatomical feature of interest, placed on the skin of the patientadjacent to the anatomical feature(s) of interest, and/or placed onclothing of the patient adjacent to the anatomical feature(s). In someembodiments, implantable sensors are preferred. In some instances,sensors are used both inside and outside of the patient's body.Implantable sensors facilitate direct contact with the anatomicalfeature(s) of interest or at least provide substantially closerplacement to the anatomical features than sensors that remain outsidethe patient's body. In that regard, implantable sensors facilitate theaccurate detection of the position of internal anatomical features thatcannot be accurately determined with external sensors alone. Theremaining description of the present method 500 will be described withrespect to implanted sensors, however, no limitation is intendedthereby.

In some embodiments, the implantable sensors are configured forengagement with bone. In that regard, the implantable sensors are partof a bone screw or other bone fixation device in some embodiments. Inother embodiments, the implantable sensors are secured to the bone via abiocompatible adhesive or epoxy, a structural fixation device (screw,staple, etc.), combinations thereof, and/or other otherwise secured tothe bone. Generally, engaging the implantable sensors with bone providesa fixed orientation between the sensor and the bone, which allows a goodcorrelation between the position of the sensor and the position of thebone. In other embodiments, the sensors are configured for engagementwith softer tissues. In such embodiments, the sensors include featuresto prevent unwanted movement of the sensors relative to the tissue. Thesensors are introduced via a guidewire, needle, catheter, tube, and/orother suitable implantation means. Preferably, the sensors are implantedusing a minimally invasive procedure and in some instances are implantedpercutaneously. In other embodiments, the sensors are implanted as partof a larger surgical procedure and, therefore, are implanted throughnon-minimally invasive means.

The sensors are utilized for tracking the position of one or moreanatomical features. In that regard, one or more sensors are placedadjacent each anatomical feature of interest. In some embodiments thesensors are configured for identifying the location and tracking themotion of one or more of the following anatomical features or partsthereof: heels, ankles, knees, hips, iliac crests, sacrum, pelvis,spinal column, spinal column regions, vertebrae, transverse processes,spinal processes, clavicles, and other anatomical features. In oneparticular embodiment, the sensors are placed on a plurality ofvertebrae along the spine. The relative motion of the sensors placed oneach of the plurality of vertebrae are utilized to obtain relativeorientation and motion information for the sensors, which in turn can beextrapolated to the vertebrae. The actual anatomical features for whichsensors are located adjacent to depends on numerous factors includingphysician preference, patient condition, treatment plans, surgicalprocedures, and other factors. In some embodiments, the anatomicalfeature(s) of interest are selected by the treating physician ortechnician.

After the sensors have been introduced at step 502, the method 500continues at step 504 in which the motion profile of the anatomicalfeatures is tracked or measured. In some embodiments, the motion profileis tracked by creating a model of the patient's anatomy and simulatingthe patient's motion profile based on the sensor data. In that regard,an imaging step is performed in some embodiments as part of creating themodel. In orthopedic applications, the imaging focuses on the relevantskeletal structures of the patient, which are typically the anatomicalfeatures of interest as well. Generally speaking, the imaging mayinclude x-ray, fluoroscopy, and/or CT scans. X-ray machines may beutilized to obtain snap-shot images of the patient's skeletal structure.Fluoroscopy machines may be utilized to obtain real-time images of thepatient's skeletal structure, which may be beneficial in correlating themodel to the motion profile in some instances.

Generally, the data from the imaging protocol is utilized to create themodel. In one particular embodiment, the data from the imaging protocolis utilized to segment the model into the individual anatomical featuresof the patient. In that regard, a motion joint is modeled by thecombination of individual bones that come together to form the joint. Insome embodiments, the dimensions of the implanted sensor are known andutilized to correlate bone position to the sensor position. Further, theorientation of the sensor to the bone is established by an asymmetry inthe structure of the sensor that is identifiable through the imagingprotocol. Accordingly, in some embodiments the known dimensions andfeatures of the implanted sensors are utilized in creating the model ofthe patient's anatomical features. The model is either a 3-D or 2-Drepresentation of the patient's anatomy. In some embodiments, the modelis animated to illustrate the motion profile of the patient's anatomy.In other embodiments, the model is simply a statistical representationof the patient's anatomy and does not provide a visualization. In thatregard, a computer system is utilized to analyze the patient's motionsequence and associated data to provide suggested implant parameters andmodifications thereto.

In some embodiments, the motion profile of the patient's anatomicalfeatures is determined by putting the patient through a diagnosticprotocol. In that regard, the diagnostic protocol is a series ofmovements that the patient is put through that utilizes the anatomicalfeatures of interest. In some embodiments, the diagnostic protocol isperformed to measure joint motion and/or relative motion between theanatomical features. Accordingly, the diagnostic protocol oftencomprises a natural movement such as walking, sitting, standing, lyingdown, or other common movements. However, in other embodiments thediagnostic protocol comprises a specific series of movements thatinclude at least some movements that are not performed on a regularbasis. The precise movements or structure of the diagnostic protocoldepends on the anatomical features of interest and/or the treatingphysician's preference.

The implanted sensors are utilized to track the motion profile of thepatient's anatomy through the diagnostic protocol. In some embodiments,the relative motion between the implanted sensors are utilized tomonitor the patient's motion profile. That is, the movement of eachsensor with respect to the other sensors is tracked and utilized todetermine the relative motion between the anatomical features associatedwith each sensor. In some embodiments, the absolute positions of thesensors are tracked and correlated to the motion of the anatomicalfeatures. That is, the positions of the sensors are tracked with respectto a reference point(s) (e.g., signal receiver(s)), which can in turn beutilized to determine the motion of the anatomical features. In someembodiments, the positions of the sensors are monitored using wirelesstelemetry to measure the distances between each sensor. For example, insome instances each sensor is registered with one or more signalreceivers and the position of the sensor is tracked using wirelesstelemetry. Based on the communication of the sensor with the signalreceiver a time of flight calculation can be made to triangulate theposition of the sensor with respect to the signal receivers over time.The positions of each of the sensors can then be compiled to identifythe relative motion sequence of the anatomical features with respect toone another. Taken together, the motion sequence of the anatomicalfeatures of interest is established.

In other embodiments, an electromagnetic measurement system is utilizedto track the positions of the implantable sensors during the diagnosticprotocol. For example, the electromagnetic measurement system can detectthe presence of sensors excitable by an electromagnetic field todetermine the position of the anatomical features associated with thesensor. The electromagnetic measurement system utilizes a computersystem to calculate the 3-D position of the anatomical feature(s) basedon the position of the sensors. In some embodiments, the electromagneticmeasurement system is configured to detect the position of sensors in afixed volume of space. In that regard, in some embodiments the fixedvolume of the electromagnetic measurement system is sufficient to obtainthe position of all relevant anatomical features of a patient. In otherembodiments, however, the fixed volume may be sufficient to obtain 3-Dpositions of only some anatomical features of a patient. Where the fixedvolume is sufficient to obtain 3-D positions of some, but not all of thepatient's anatomical features, a portion of the electromagnetic measuresystem is moveable such that the 3-D positions of all the anatomicalfeatures of most interest can be obtained.

In some instances, the sensor systems of the present disclosure areself-calibrating. In that regard, the relative orientation and/orposition of the sensors is determined by the sensors and associatedcomponents without need of manual input from a user or medicalpersonnel. For example, in one embodiment, the implantable sensorsinterface with a software suite for tracking the positioning of thesensors. Each of the sensors provides an initial coordinate position. Insome instances the initial coordinate position will be an arbitrarycoordinate. In other instances, the initial coordinate position will berelative to a known point of reference (e.g., a main sensor, a referencepoint in the room, an anatomical reference point of the patient, orotherwise). Based on the initial coordinate position the software suitewill reset or zero out the location of each of the sensors to thisstarting point. Accordingly, subsequent movements can be compared tothis initial starting position.

After the motion profile of the anatomical features has been tracked atstep 504, the method 500 continues at step 506 in which the motionprofile is analyzed. In some embodiments, the motion profile is analyzedby updating the model and/or creating a model to simulate the detectedmotion profile. As described above the model is a 3-D and/or 2-Danimated model of the patient's anatomy for visualizing the patient'sanatomy in some instances. In other instances, the model is simply anumerical or statistical representation of the patient's motion profilethat is utilized by a computer system to analyze the patient'sanatomical motion profile. In some embodiments, the animated model isused to highlight the problem areas and/or times in the patient'sanatomical motion sequence. In that regard, the model includes layers ofanatomical features that are selectively included or removed. Forexample, in one embodiment the patient's motion anatomy is grouped intolayers according to types of anatomical tissue, such as bones,cartilage, ligaments, tendons, muscles, and/or combinations thereof. Theanimated model then simulates the motion according to each grouping ofanatomical tissue and the interactions therebetween.

The motion sequence of the patient is analyzed by a computer systemand/or medical personnel. In some embodiments, the model itself includesfeatures to allow medical personnel and/or a computer system to analyzethe patient's motion sequence. In other embodiments, a separate softwaresuite or program is utilized to analyze the patient's motion sequence.In that regard, in some embodiments the model includes a stress gridoverlay that indicates potential areas of increased stress or strain onthe patient's anatomy, such increased muscle activity; overstretching ofmuscles, ligaments, and/or tendons; friction between bones; and/or otherareas of stress/strain. In some embodiments the model allows forzooming, panning, or otherwise changing the orientation of the view ofthe patient's anatomy. A user adjusts the orientation to better observeor isolate potential problem areas. Similarly, the animated model allowsa user to pause, rewind, slow down, and/or speed up simulation of amotion sequence to better observe a potential problem. Further, themodel allows 3-D and/or 2D tracking of specific anatomical featuresthrough the motion sequences. In some embodiments, the animated modelhighlights potential problem areas automatically based on a comparisonto a standardized model. For example, the system identifies anatomicalfeatures with a motion sequence outside of a predetermined range in someinstances. In that regard, the standardized model and/or predeterminedrange of normal motion are at least partially defined by a generalpatient population.

In some embodiments, the treating physician or medical personnelhighlights potential problem areas based on their observations of thepatient's motion sequence. In some embodiments, the problem areas areidentified by a computer system and/or medical personnel by recognizingan abnormal motion pattern(s). In some instances, the abnormal motionpatterns are grouped into motion signatures that are indicative of aspecific type or grouping of medical conditions. Each of the motionsignatures, in turn, is associated with appropriate medical treatmentoptions for correcting the medical condition(s) associated with themotion signature.

Based on the analysis of the patient's motion profile at step 506, themethod 500 continues at step 508 in which the treatment parameters aremodified or defined in an effort to correct any problems in the motionprofile. In some embodiments, the treatment parameters comprise theplacement, orientation, stiffness, and/or other aspects of an implant.In that regard, in some embodiments the diagnostic protocol is performedduring the surgical procedure such that the implant parameters aremodified without need for a subsequent medical procedure. For example,in one embodiment the method 500 is utilized to balance a kneearthroplasty. In other embodiments, the diagnostic protocol is performedpost-surgery in an effort to maintain and/or improve the effectivenessof the treatment. In that regard, when modifications to the implantparameters are suggested, a revision surgery may be required. However,in some embodiments, the implant includes features that allownon-invasive adjustment of the implant. For example, in some embodimentsthe implant includes one or more actuators to adjust the position of theimplant relative to a fixation device. In other embodiments, the implantincludes one or more actuators to adjust the relative stiffness of theimplant. In some embodiments, the implanted sensors are utilized with adynamic fixation system such as that described in U.S. patentapplication Ser. No. 11/356,687 filed Feb. 17, 2006 and titled “Sensorand Method for Spinal Monitoring,” herein incorporated by reference inits entirety. In that regard, in such embodiments the dynamic actuatorsthat control the dampening force of the implant are adjusted based onthe parameters as indicated by the feedback of the sensors. The sensorsare utilized to adjust other adjustable implants. In other embodiments,the treatment plan does not include an implant and, therefore, themodified parameters are not related to the implant.

After the treatment parameters have been modified and/or defined in step508, the method 500 returns to step 504 where the motion profile isagain monitored and then analyzed at step 506. If the analysis againdetects problems in the motion profile, then the method returns to step508 for additional modification of the treatment parameters.Accordingly, steps 504, 506, and 508 are iterated until a satisfactorymotion profile is established with selected the treatment parameters.When the treatment parameters have been defined to achieve the desiredmotion profile, then the method 500 concludes with step 510 in which thetreatment parameters are finalized. The finalized treatment parametersare then implemented. In some embodiments, steps 504, 506, and 508 arerepeated at various intervals after an initial treatment to maintain thedesired motion profile of the patient's anatomy.

In some embodiments, an implant, prosthesis, fixation element, or otherdevice is instrumented with a plurality of sensing elements to monitorvarious conditions within a patient. For example, referring morespecifically to FIGS. 30 and 31 shown therein is a device 520 accordingto one aspect of the present disclosure. In the illustrated embodiment,the device 520 comprises a bone anchor or screw configured forengagement with a bony structure of a patient. This illustratedembodiment is merely for exemplary purposes and numerous other types ofimplants may be similarly fitted with multiple sensors in otherembodiments. Generally, the device 520 includes a head portion 522 and abody portion 524. The head portion 522 is configured for engagement withan insertion instrument and, in some embodiments, has a substantiallyhex-shaped profiled for mating with a hex-shaped driver. In otherembodiments, the head portion 522 has other profiles and/or includesrecessed portions for engaging with an insertion instrument or driver.The body portion 524 comprises a series of threads for engaging with abone. The device 520 also includes a opening 526 for housing the sensorsextending along its length from a proximal portion 528 to a distalportion 530. In other embodiments, the opening 526 may extend along onlya portion of the device 520. For example, in some embodiments theopening is completely contained within the body portion 524 of thedevice 520. In some embodiments, the device does not include a singleopening for housing the sensors, but contains multiple openings forhousing the sensors. In some embodiments, the sensors are positioned onthe outer surfaces of the device. In some instances an implant isinstrumented or fitted with various sensors capable of detectingphysical parameters.

A plurality of sensors are positioned within the opening 526. In theillustrated embodiment, a chemical sensor 532, a multi-purpose sensor534, an accelerometer 536, and a pressure sensor 538 are included withinthe opening 526. In the current embodiment, if the device 520 wassecured to a vertebra the sensors 532, 534, 536, and 538 could beutilized to monitor vertebral motion, load/stress, and/or the existenceor quantity of particular proteins adjacent to the site. In that regard,the position, order, or orientation of the sensors with respect to thedevice 520 may also affect the parameters that are monitored. Forexample, in some embodiments the pressure sensor is positionedsubstantially in the head portion 522 of the device. In suchembodiments, the pressure sensor is utilized to monitor the patient'sheart rate, swelling, and/or other pressure related parameters externalto the vertebral joint. Accordingly, the placement and orientation ofthe sensors relative to one another, the device, and/or anatomicalfeatures is selected based on the parameters to be monitored.

Generally, the sensors 532, 534, 536, and 538 are selected from sensorsthat are able to monitor various physical parameters associated with thepatient's anatomical features and/or treatment device, such as pressure,linear displacement, angular displacement, torque, velocity,acceleration, temperature, or pH. In some instances the sensor may be amulti-purpose sensor in that it can be programmed or modified to monitorvarious parameters. A pressure sensor may, for example, use Wheatstonebridge based strain gauge technology. Alternative pressure sensors mayinclude inductive or capacitive measurement systems. A lineardisplacement sensor may, for example, use linear variable differentialtransformer (LVDT) technology to measure linear displacements. Likewise,an angular displacement may, for example, use rotational variabledifferential transformer (RVDT) technology to measure angulardisplacement. An acceleration sensor may, for example, include anaccelerometer. It is understood that multiple sensors of various typesmay be used in a single implant to measure different physicalparameters. The particular types of sensors to be included within thedevice 520 depends on the selected treatment, the anatomical feature(s)being treated, physician preference, and/or other factors. In someinstances, the device is fully assembled with a predetermined collectionof sensors pre-operatively. In other instances, the device is modularsuch that sensors having the desired parameters are selected from a kitcomprised of a plurality of sensors and are inserted into the devicepre-operatively or intra-operatively.

In some embodiments, the sensors are utilized to measure anatomicaland/or physiological data that is then transferred externally.Accordingly, in embodiments where multiple sensors are utilized it canbe necessary to distinguish between the various sensors. In oneembodiment, the communication frequencies of the sensors aredifferentiated to decrease airwave clutter and/or prevent data frombeing mixed up. In some embodiments, the sensors are coded toautomatically establish sensor-to-sensor relationships. Accordingly, anetwork of sensors can be created by the sensor-to-sensor relationships.The sensor-to-sensor communications are utilized to increase efficiencyand/or improve processing times in some instances. In some embodimentseach sensor includes a unique identification that is associated with it.In some embodiments, each sensor is assigned a unique serial number oridentification number. The serial number or identification number isthen transferred along to the external device with any data.Accordingly, any data received from the sensors is associated with aparticular sensor. This allows for easy association of the received datato the sensors by checking the serial number accompanying the data.

Further, in some instance the serial number of each sensor is furtherassociated with the patient. Accordingly, even the raw data receivedfrom the sensors can be associated with the correct patient. In thatregard, the serial number is used to access patient data in someembodiments. For example, patient data such as prior diagnoses, priortreatments, height, weight, blood pressure, etc. can be provided to themedical personnel treating the patient. In other instances, previousdiagnostic studies and/or data sets from the sensors can be provided tothe medical personnel treating the patient. In that regard, the priordata sets are compared to the current data sets in some instances. Insome instances, the serial number is associated with a patient, but allprivate information regarding the patient is disassociated from theserial number. For example, the serial number may be associated with thegeneral characteristics of the patient (such as age, height, weight,medical condition, treatment plan, etc.), but private information (suchas the patient's name, address, social security number, etc.) are notassociated with the serial number. Accordingly, the sensors are utilizedin some embodiments for providing data without any personal informationto a database for later use. Such a system could be utilized tostreamline referrals, reduce costs, and/or generally improve patientcare. In some embodiments, the sensors are utilized in an ER or othersituation where the treating medical personnel knows nothing or verylittle about the patient and the patient's primary care physician isunavailable. The data associated with the sensor can be utilized toprovide additional information to the medical personnel that may becrucial in determining the appropriate treatment options for the patientin the emergency.

Generally, the data collected from the various sensors is used tomonitor the effectiveness (or lack thereof) of the treatment, modify thetreatment plan, monitor the position of an implanted device, and/orotherwise monitor the anatomical area of interest. The data from thesensors can be stored in a database for analysis and consideration inlater patient treatments. In some instances the data is utilized torefine the design of the device or implant. For example, understandingforces exerted on a device and the resulting pressure concentrationswithin the device may permit design changes that can reduce the weightof the implant and/or localize material strength though materialselection or material thickness.

In some instances, one or more of the sensors positioned within thedevice 520 are selectively activated and de-activated. In someembodiments, the device 520 is reprogrammable such that the activesensors can be changed or modified. In that regard, allowing the device520 to be reprogrammed multiple times can extend sensor life, improvedata exchange efficiency, and/or minimize power consumption.

Referring now to FIG. 32, shown therein is a system 540 for monitoringimplant loosening according to one embodiment of the present disclosure.A vertebra 542 has been engaged by a bone fixation device 544, as shown.A sensor 546 is positioned within or on the bone fixation device 544. Insome instances, the bone fixation device 544 is part of a largertreatment system (not shown). For example, in some instances the bonefixation device 544 is part of a rod and screw system for limiting themotion of vertebrae. In other embodiments, the bone fixation device 544is utilized as part of a dynamic fixation system. A sensor 548 ispositioned adjacent to the bone fixation device 544 and the sensor 546.In some embodiments, the sensor 548 is fixed with respect to thevertebra 542. In that regard, the sensor 548 may itself engage the boneand/or the sensor 548 may be attached or imbedded within a housing thatengages to the bone. The sensors 546 and 548 are utilized to monitorand/or detect any loosening of the bone fixation device 544 relative tothe vertebra 542.

In that regard, the sensors 546 and 548 are accelerometers in someembodiments. The relative motion of the sensors 546 and 548 with respectto one another is detected. If the motion patterns of the sensors 546and 548 are substantially similar, then the bone fixation device 544 issubstantially fixed with respect to the vertebra. This is because thesensor 546 is fixed with respect to the bone fixation device and thesensor 548 is fixed with respect to the vertebra 542. However, if themotion patterns of the sensors 546 and 548 are divergent, then this canbe an indication of loosening of the bone fixation device 544 relativeto the vertebra. In that regard, the magnitude of the divergence betweenthe motion patterns of the sensors 546 and 548 can be indicative of theamount or degree of loosening of the bone fixation device 544. In otherembodiments, the relative angles of the sensors with respect to oneanother is monitored. If the fixation device 544 remains substantiallyfixed to the vertebra 542, then relative angles of the sensors 546 and548 remains substantially fixed as well. However, if the fixation device544 has loosened, then the fixation device may toggle with respect tothe vertebra 542 and the relative angles of the sensors 546 and 548 willchange. Accordingly, the relative angles of the sensors 546 and 548 areutilized in some embodiments to detect loosening. In some instances, thedegree of loosening is monitored over time. The loosening informationmay be utilized to determine the need for additional treatment and/orrevision surgery to correct the loosening. In some embodiments, morethan one sensor is fixed relative to the vertebra 542 and/or thefixation device 544. Use of multiple sensors can prevent a falsedetection of loosening where the sensor itself has become loose for somereason.

While the detection of loosening has been described with respect to abone fixation device, similar concepts are utilized for monitoringloosening of other implants, including other fixation devices,prosthetic device, and/or sensors. Further, the detection of looseningis not limited to the spinal region, but is utilized throughout the bodywhere implants are fixed with respect to anatomical features. Forexample, referring now to FIG. 33, shown therein is a system 550 formonitoring implant loosening according to another embodiment of thepresent disclosure. In particular, the system 550 illustrates a longbone 552 that has received an implant 554, as shown. In some instances,the implant 554 comprises an intramedullary rod or nail. The implant 554includes a sensor 556 therein. The bone 552 includes a pair of sensors558 and 560. As discussed above, the use of multiple sensors—such assensors 558 and 560—provides a redundancy that helps prevent falsedetection of implant loosening. In other embodiments, the implant 554also includes multiple sensors. In some embodiments, the implant 554includes a sensor proximal to each end of the implant. Generally, thesensors 556, 558, and 560 are utilized to detect loosening of theimplant 554 with respect to the bone 552 in a similar manner asdescribed above.

In some instances, the implantable sensors and/or implants including thesensors are in communication with a device or system for remotelycommunicating data obtained by the sensors to a medical facility ormedical personnel. For example, in some instances the implantablesensors and/or implants are configured for communication with a systemsuch as the CARELINK system from Medtronic. In some instances, data fromthe sensors is transferred to the medical facility or personnel at aregular interval (e.g., once a day, week, or otherwise). In otherinstances, data from the sensors is transferred to the medical facilityor personnel upon the sensors sensing an abnormality or change in one ormore of the conditions monitored by the sensors. In some instances, thesensors and/or implants are associated with reservoirs ofpharmaceuticals for controlled dispensing depending on the sensedconditions. Such reservoirs are utilized for pain management, toencourage healing, promote tissue growth, or otherwise in someinstances. In some instances, one or more devices or methods asdescribed in U.S. patent application Ser. No. 11/217,693 filed Sep. 8,2006 entitled “Controlled Release Systems and Methods for OstealGrowth,” U.S. patent application Ser. No. 11/517,771 filed Sep. 8, 2006entitled “Controlled Release Devices for Fusion of Osteal Structures,”and/or U.S. patent application Ser. No. 11/410,216 filed Sep. 8, 2006entitled “Controlled Release Systems and Methods for IntervertebralDiscs,” each of which is incorporated by reference herein in itsentirety. In other instances, the implants are adjustable based on thesensed conditions. For example, in some instances the stiffness and/ordampening of an implant is adjusted based on the sensed conditions. Insome instances, devices as described in U.S. patent application Ser. No.12/048,627, filed Mar. 14, 2008 entitled “Intervertebral Implant andMethods of Implantation and Treatment,” hereby incorporated by referencein its entirety, are utilized. In some instances, devices similar tothose described in PCT/US2005/020116 filed Jun. 8, 2005 entitled“Prosthetic Intervertebral Spinal Disc With Integral Microprocessor,”hereby incorporated by reference in its entirety, are utilized.

In one embodiment, a method of patient assessment and outcome modelingcomprises: obtaining patient characteristic information from a currentpatient; defining a plurality of therapeutic factors based on thecharacteristic information of the current patient; weighting thetherapeutic factors; accessing at least one database having medicalrecords of prior patients, the medical records including at least priorpatient characteristic information, prior patient treatment plan, andprior patient outcome; comparing the weighted factors of the currentpatient to the medical records of the prior patients to identify one ormore relevant prior patient records; retrieving at least a portion ofthe relevant prior patient records, the portion including at least theprior patient treatment plan and the prior patient outcome; andperforming a simulation of at least one of the prior patient treatmentplans based on the current patient's characteristic information.

In some instances, the method further comprises identifying at least oneavailable treatment plan for the current patient. In some instances, thedatabase includes information collected from one or more treatmentstudies. In some instances, the steps of accessing at least onedatabase, comparing the weighted factors of the current patient to themedical records of the prior patients, retrieving at least a portion ofthe relevant prior patient records, and performing the simulation areexecuted electronically. In some instances, the steps of accessing atleast one database, comparing the weighted factors of the currentpatient to the medical records of the prior patients, retrieving atleast a portion of the relevant prior patient records, and performingthe simulation are executed over a computer network. In some instances,at least one of the steps of accessing at least one database, comparingthe weighted factors of the current patient to the medical records ofthe prior patients, retrieving at least a portion of the relevant priorpatient records, and performing the simulation is executed remotely overa computer network. In some instances, at least two available treatmentplans are identified and further comprising ranking the at least twoavailable treatment plans. In some instances, the method furthercomprises performing a simulation of the at least two availabletreatment plans, where the ranking is at least partially based on thesimulations. In some instances, the ranking is at least partially basedon the success of the available treatment plans for the one or morerelevant prior patient records. In some instances, the prior treatmentsand the administered treatments include a spinal surgical procedure. Insome instances, the patient characteristic information includes patientcharacteristic information obtained from diagnostic tests.

In one embodiment, a system for pathology assessment, treatment, andoutcome modeling comprises: a database having a plurality of records ofprior patients, the records including patient characteristicinformation, treatment information, and outcome information; and atleast one processing system operatively connected to the database, theat least one processing system comprising a diagnosis module, a modelingmodule, and a treatment module; where the diagnosis module is configuredto receive and weight current patient information, compare the currentpatient information to the plurality of records of the database, andretrieve records of prior patients with similar characteristicinformation from the database, the treatment module is configured toidentify available treatment options for the current patient, and themodeling module is configured to simulate the available treatmentoptions for the current patient, wherein the simulation is at leastpartially based on the outcome information from the records of priorpatients. In some instances, the diagnosis module is configured tomonitor the outcome of a treatment of the current patient. In someinstances, the database is remote from at least one of the diagnosismodule, the modeling module, and the treatment module.

In one embodiment, a method for patient assessment and outcomeprediction comprises: obtaining a plurality of therapeutic factors froma current patient, said factors based at least partially on the currentpatient's physical characteristics, pathology, and desired therapeuticoutcomes; weighting the therapeutic factors; accessing at least onedatabase having records of prior patient treatments, including priorpatient therapeutic factors, treatment plans, and treatment outcomes;comparing the therapeutic factors of the current patient with the priorpatient therapeutic factors in the records of the database to identifyprior patients with similar therapeutic factors; retrieving from thedatabase at least a portion of one or more records of prior patientswith similar therapeutic factors; identifying one or more availabletreatment plans for the current patient based at least in part on therecords of the prior patients with similar therapeutic factors; andpredicting a likelihood of success for each of the one or more availabletreatment plans for the current patient.

In some instances, the available treatment plans are identified based onthe success of the treatment plans with prior patients with similartherapeutic factors. In some instances, method further comprisessimulating the one or more available treatment plans based on thecurrent patient's physical characteristics, pathology, and desiredtherapeutic outcomes. In some instances, the method further comprisesselecting a treatment plan at least partially based on the simulating ofthe one or more available treatment plans. In some instances, selectinga treatment plan is at least partially based on the treatment outcomesof the prior patients with similar therapeutic factors. In someinstances, accessing the at least one database includes accessing the atleast one database from a remote location.

In one embodiment, a method for identifying available treatment optionsfor a patient having an increased likelihood of success, comprising:obtaining a plurality of therapeutic factors from a current patient,said factors based at least partially on the current patient's physicalcharacteristics, pathology, and desired therapeutic outcomes; weightingthe therapeutic factors; accessing at least one database having recordsof prior patient treatments, including prior patient therapeuticfactors, treatment plans, and treatment outcomes; comparing thetherapeutic factors of the current patient with the prior patienttherapeutic factors in the records of the database to identify priorpatients with similar therapeutic factors; retrieving from the databaseat least a portion of one or more records of prior patients with similartherapeutic factors; and identifying available treatment options for thecurrent patient based at least in part on the records of the priorpatients with similar therapeutic factors.

In some instances, the available treatment options are identified basedon the success of the treatment options with prior patients with similartherapeutic factors. In some instances, the method further comprisessimulating the one or more available treatment options based on thecurrent patient's physical characteristics, pathology, and desiredtherapeutic outcomes. In some instances, a specific treatment option isselected from the one or more available treatment options at leastpartially based on the simulating of the one or more treatment plans. Insome instances, the specific treatment option is selected at leastpartially based on the treatment outcomes of the prior patients withsimilar therapeutic factors. In some instances, the accessing the atleast one database includes accessing from a remote location. In someinstances, accessing the at least one database is executed remotely overa computer network. In some instances, the method further comprisesranking the available treatment options. In some instances, the rankingis at least partially based on simulating the available treatmentoptions. In some instances, the ranking is at least partially based onthe prior patient outcomes. In some instances, the patientcharacteristic information includes patient characteristic informationobtained from diagnostic tests. In some instances, the diagnostic testsinclude imaging.

In one embodiment, a system for identifying available treatment optionsfor a current patient having an increased likelihood of successcomprises: at least one local database having a plurality of records ofprior local patients, the records including patient characteristicinformation, treatment information, and outcome information; at leastone remote database having a plurality of records of prior remotepatients, the records including patient characteristic information,treatment information, and outcome information; at least one processingsystem operatively connected to the local and remote databases, the atleast one processing system comprising a diagnostic module, a modelingmodule, and a treatment module; where the diagnostic module isconfigured to receive and weight current patient information, comparethe current patient information to the plurality of records of in thelocal and remote databases, and retrieve records of prior patients withsimilar characteristic information from the local and remote databases,the treatment module is configured to identify available treatmentoptions for the current patient based at least partially on the recordsretrieved from the local and remote databases by the diagnostic module,and the modeling module is configured to simulate the availabletreatment options for the current patient identified by the treatmentmodule, wherein the simulation is at least partially based on theoutcome information from the records of prior patients retrieved fromthe local and remote databases. In some instances, the treatmentinformation stored in the local and remote databases includes medicalproducts used in the treatment. In some instances, the processing systemis operatively connected to the local database in order to store currentpatient information in the local database. In some instances, the localdatabase is at least partially accessible by a remote processing system.In some instances, private information stored in the local database isnot accessible by a remote processing system.

In one embodiment, a method for identifying available treatment optionscomprises: accessing at least one database having records of priorpatients, including prior patient treatment plans and treatmentoutcomes; identifying prior patients with similar characteristics to acurrent patient; retrieving from the database at least a portion of therecords of prior patients with similar characteristics to the currentpatient, the portion of the records including the treatment plans andtreatment outcomes; and identifying successful treatment plans of priorpatients based on the treatment outcomes. In some instances, the methodfurther comprises modeling the successful treatment plans identifiedbased on the current patient's characteristics. In some instances, themethod further comprises ranking the successful treatment plans at leastpartially based on the modeling.

In one embodiment, a method of obtaining and analyzing patientinformation for diagnosis and treatment comprises: identifying at leastone patient symptom; selecting at least one patient category associatedwith the at least one patient symptom; obtaining data corresponding tothe at least one patient category; providing the obtained data to asoftware application; analyzing the obtained data with the softwareapplication; and providing a summary of the software applicationanalysis for use in diagnosing the patient's medical condition andidentifying available treatment options.

In some instances, selecting the at least one patient category comprisesselecting a patient category from a predefined set of patientcategories. In some instances, each patient category of the predefinedset of patient categories includes an associated data collection setthat defines a plurality of data items corresponding to the patientcategory. In some instances, obtaining data corresponding to the atleast one patient category comprises performing a diagnostic test. Insome instances, obtaining data corresponding to the at least one patientcategory comprises asking the patient a series of questions. In someinstances, obtaining data corresponding to the at least one patientcategory comprises obtaining data from a previous medical exam. In someinstances, the data from the previous medical exam is provided by areferring medical institution. In some instances, the method furthercomprises comparing the summary to a prior patient data set to identifypreviously successful treatment plans of prior patients in a similarpatient category. In some instances, the method further comprisesmodeling the previously successful treatment plans based on the at leastone patient symptom. In some instances, the method further comprisesselecting a treatment plan for the current patient based at leastpartially the comparison. In some instances, the selected treatment planis a previously successful treatment plan of a prior patient in asimilar patient category.

In one embodiment, a method of obtaining and analyzing patientinformation for diagnosis and treatment comprises: submitting a patientto diagnostic testing; obtaining results from the diagnostic testing;categorizing the patient based on the results from the diagnostictesting; obtaining additional data regarding the patient, the data beingassociated with the categorization of the patient; providing theobtained data and the results from the diagnostic testing to a softwareapplication; analyzing the obtained data and results from the diagnostictesting with the software application; and identifying at least oneavailable treatment option for the patient based on the analysis.

In some instances, submitting the patient to diagnostic testing includesimaging. In some instances, analyzing the data and results comprisescreating a model of a portion of the patient's anatomy. In someinstances, identifying at least one available treatment option comprisessimulating the at least one treatment option within the model. In someinstances, identifying at least one available treatment option furthercomprises identifying successful treatment options of previous patientswith a similar categorization. In some instances, categorizing thepatient comprises selecting a category from a predefined set ofcategories. In some instances, categorizing the patient is performed bya computer system based on the results of the diagnostic testing. Insome instances, obtaining additional data regarding the patientcomprises performing additional diagnostic tests. In some instances,providing the obtained data and the results from the diagnostic testingto the software application comprises sending the data and results overa computer network.

In one embodiment, a method of visualizing and analyzing anatomicalmotion comprises: providing a plurality of implantable sensors, eachsensor configured for implantation adjacent to an anatomical feature ofa patient; tracking the positions of the implantable sensors as thepatient is put through a diagnostic motion protocol; correlating thepositions of the implantable sensors to the positions of the anatomicalfeatures of the patient adjacent to the sensors; visualizing a motionsequence of the anatomical features according to the positions of theanatomical features from the diagnostic motion protocol; and analyzingthe motion sequence of the anatomical features to identify a medicalproblem.

In some instances, tracking the positions of the implantable sensorscomprises using wireless telemetry communication between the sensors andat least one receiver. In some instances, the positions of theimplantable sensors are determined by triangulation. In some instances,tracking the positions of the implantable sensors comprises monitoringthe relative movement between the sensors. In some instances, therelative movement between the sensors is monitored by comparingaccelerometer data from the sensors. In some instances, the methodfurther comprises securely attaching each of the implantable sensors toa portion of the adjacent anatomical feature. In some instances,securely attaching the implantable sensor comprises threadingly engaginga housing of the sensor with a bone. In some instances, visualizing themotion sequence of the anatomical features comprises creating ananimated model of the anatomical features. In some instances, analyzingthe motion sequence comprises comparing the animated model to astandardized model to identify an abnormality in the motion sequence. Insome instances, the method further comprises correlating the abnormalityin the motion sequence to identify the medical problem. In someinstances, the method further comprises imaging the patient with thesensors implanted. In some instances, the method further comprisesdetermining a relative orientation between each of the implantablesensors and each of the adjacent anatomical features based on theimaging. In some instances, the method further comprises using theimaging and relative orientations to create an initial model of thepatient's anatomical features. In some instances, the method furthercomprises updating the model based on the positions of the anatomicalfeatures from the diagnostic motion protocol.

In one embodiment, a system for visualizing and analyzing anatomicalmotion comprises: a plurality of implantable sensors, each sensorconfigured for implantation adjacent to an anatomical feature of apatient; a monitoring system in communication with the implantablesensors, the monitoring system configured to track the positions of thesensors within the patient during a diagnostic motion protocol; at leastone processing system in communication with the monitoring system, theat least one processing system comprising a modeling module configuredto create an animated model of the patient's anatomical features basedat least partially on the positions of the sensors as tracked by themonitoring system during the diagnostic motion protocol.

In some instances, each of the plurality of implantable sensors areconfigured for engagement with a bone structure. In some instances, thesystem further comprises an imaging device in communication with the atleast one processing system, wherein the animated model is at leastpartially based on data obtained by the imaging device. In someinstances, each of the plurality of sensors comprises an asymmetricalprofile such that an orientation of the sensor with respect to theadjacent anatomical feature is detectable by the imaging device. In someinstances, the monitoring system comprises a wireless telemetry receiversystem configured for communication with the plurality of implantablesensors. In some instances, the monitoring system comprises a pluralityof receivers and is configured to determine the positions of the sensorsvia triangulation.

In one embodiment, a method of performing a surgical procedure usingimplantable sensors comprises: providing one or more implantablesensors, each sensor configured for implantation adjacent to ananatomical feature of a patient; imaging the patient to determine therelative positions of the one or more implantable sensors relative tothe anatomical features of the patient; inserting an implant adjacent toat least one of the anatomical features; and tracking the position ofthe implant relative to the at least one anatomical feature during theinserting of the implant using the implantable sensors.

In some instances, at least one of the anatomical features is avertebra. In some instances, at least one of the implantable sensorscomprises a housing having a bone engaging portion. In some instances,at least one of the implantable sensors comprises an asymmetricalprofile such that an orientation of the sensor with respect to theadjacent anatomical feature is detectable from the imaging. In someinstances, the implant includes a sensor therein and wherein trackingthe position of the implant comprises tracking the relative position ofthe sensor within the implant to at least one of the implantablesensors. In some instances, inserting the implant comprises grasping theimplant with a surgical tool. In some instances, the surgical toolincludes a sensor therein and wherein tracking the position of theimplant comprises tracking the relative position of the sensor withinthe surgical tool to at least one of the implantable sensors. In someinstances, tracking the position of the implant comprises visuallymonitoring the insertion of the implant. In some instances, the methodfurther comprises monitoring the position of the implant relative to theat least one anatomical feature using the implantable sensors afterinsertion of the implant. In some instances, the implant comprises aspinal implant. In some instances, the implant comprises an artificialdisc. In some instances, tracking the position of the implant relativeto the at least one anatomical feature comprises tracking the relativeposition of a sensor associated with the implant to at least one of theplurality of implantable sensors. In some instances, the sensorassociated with the implant is positioned within the implant. In someinstances, the sensor associated with the implant is positioned in asurgical tool for inserting the implant. In some instances, the implantis inserted using an image-guided system.

In one embodiment, a method of inserting a spinal implant comprises:providing at least one sensor, the at least one sensor being positionedwithin a housing having a bone engaging portion and an asymmetrical headportion; engaging the bone engaging portion of the housing with avertebra; imaging the patient to determine the relative position of thesensor relative to the vertebra using the asymmetrical head portion ofthe housing as a guide; inserting an implant adjacent to the vertebra;and tracking the position of the implant relative to the vertebra bycorrelating the relative position of the implant to the sensor to thevertebra. In some instances, the implant includes a sensor therein andwherein tracking the position of the implant comprises tracking theposition of the sensor within the implant relative to the sensorpositioned within the housing. In some instances, inserting the implantcomprises using a surgical tool to guide the implant to a positionadjacent to the vertebra, the implant having a fixed relationship withrespect to the surgical tool when engaged with the surgical tool. Insome instances, the surgical tool includes a sensor therein and trackingthe position of the implant comprises tracking the position of thesensor positioned within the surgical tool relative to the sensorpositioned within the housing. In some instances, the surgical tool ispart of an image-guided system.

In one embodiment, a method of selecting implant parameters comprises:introducing one or more sensors adjacent to an anatomical feature;monitoring a motion sequence of the anatomical feature with the one ormore sensors; analyzing the monitored motion sequence of the anatomicalfeature to detect a problem in the motion sequence of the anatomicalfeature; and determining a parameter for an implant for at leastpartially correcting the problem in the motion sequence of theanatomical feature. In some instances, the method further comprisesmonitoring the motion sequence of the anatomical feature with the one ormore sensors after implantation of the implant. In some instances, themethod further comprises: analyzing the monitored motion sequence of theanatomical feature after implantation of the implant to detect aremaining problem in the motion sequence of the anatomical feature; anddetermining a modification of at least one parameter of the implant toat least partially correct the remaining problem in the motion sequenceof the anatomical feature.

In some instances, monitoring the motion sequence comprises tracking aposition of the one or more sensors. In some instances, monitoring themotion sequence comprises tracking a position of the one or more sensorswith respect to another of the one or more sensors. In some instances,introducing one or more sensors adjacent to an anatomical featurecomprises implanting the one or more sensors. In some instances,analyzing the monitored motion sequence of the anatomical featurecomprises utilizing a computer system. In some instances, utilizing thecomputer system comprises creating an animated model of the motionsequence. In some instances, detecting the problem in the motionsequence comprises comparing the animated model of the motion sequenceto a standardized model. In some instances, the anatomical feature is aspinal joint. In some instances, introducing one or more sensorscomprises securing the one or more sensors to at least one vertebra. Insome instances, the method further comprises identifying one or morespinal implants for at least partially correcting the detected problemin the motion sequence of the spinal joint. In some instances, at leastone of the one or more spinal implant is adjustable such that at leastone parameter of the spinal implant is modifiable. In some instances,the method further comprises modifying the at least one parameter of theadjustable spinal implant to substantially match the determinedparameter for correcting the problem in the motion sequence of theanatomical feature.

In one embodiment, a method of selecting a spinal implant and itsparameters comprises: introducing a plurality of sensors adjacent to apair of vertebrae defining a spinal joint; monitoring a motion sequenceof the spinal joint with the plurality of sensors; analyzing themonitored motion sequence of the vertebrae to detect an initial problemin the motion sequence of the spinal joint; determining a parameter foran implant for correcting the initial problem in the motion sequence ofthe spinal joint; identifying at least one spinal implant with theparameter for correcting the initial problem in the motion sequence ofthe spinal joint.

In some instances, the method further comprises monitoring the motionsequence of the spinal joint after implantation of a spinal implant withthe parameter for correcting the problem in the motion sequence of thejoint to detect a remaining problem in the motion sequence of the spinaljoint. In some instances, monitoring the motion sequence of the spinaljoint after implantation comprises monitoring the motion sequence withat least one sensor positioned within the spinal implant. In someinstances, monitoring the motion sequence of the spinal joint afterimplantation comprises monitoring the motion sequence with the pluralityof sensors. In some instances, the method further comprises determininga factor for an implant for correcting the remaining problem in themotion sequence of the spinal joint; and identifying at least one spinalimplant with the factor for correcting the remaining problem in themotion sequence of the spinal joint. In some instances, identifying theat least one spinal implant with the factor for correcting the remainingproblem comprises identifying a modification to the spinal implant withthe parameter for correcting the initial problem in the motion sequenceof the spinal joint.

In one embodiment, a method of detecting implant loosening comprises:providing an implant for fixedly engaging with an anatomical feature ofa patient, the implant having a first sensor secured thereto; tracking afirst motion pattern of the first sensor; tracking a second motionpattern of a second sensor secured to the anatomical feature;determining a relative motion between the first sensor and the secondsensor based on the first and second motion patterns; and identifyingimplant loosening by analyzing the relative motion between the firstsensor and the second sensor.

In some instances, identifying implant loosening comprises identifyingdifferences between the first motion pattern and the second motionpattern. In some instances, a magnitude in the differences between thefirst motion pattern and the second motion pattern is indicative of thedegree of loosening. In some instances, determining a relative motionbetween the first and second sensors comprising monitoring the relativeangle of the first sensor to the second sensor. In some instances,identifying implant loosening comprises identifying a change in therelative angle of the first sensor to the second sensor indicative ofimplant loosening. In some instances, the implant is for fixedlyengaging with a bone. In some instances, the first sensor secured to theimplant is embedded within the implant. In some instances, the implantis a spinal prosthetic. In some instances, the implant is a fixationdevice. In some instances, the implant is configured for insertion intoan intramedullary canal of a long bone. In some instances, the first andsecond sensors comprise accelerometers.

In one embodiment, a method of detecting implant loosening comprises:inserting a first sensor into a bone structure; securing the firstsensor in a fixed position with respect to the bone structure; engagingan implant with at least a portion of the bone structure, the implanthaving a second sensor positioned therein; securing the implant with theportion of the bone structure such that the second sensor issubstantially fixed with respect to the bone structure and the firstsensor; and monitoring the position of the second sensor with respect tothe first sensor to identify implant loosening.

In some instances, monitoring the position of the second sensor withrespect to the first sensor comprises monitoring the relative angle ofthe second sensor to the first sensor. In some instances, a change inthe angle between the second sensor and first sensor is indicative ofimplant loosening. In some instances, monitoring the position of thesecond sensor with respect to the first sensor comprises monitoringmotion patterns of the first and second sensors. In some instances, adifference between the motion patterns of the first and second sensorsis indicative of implant loosening. In some instances, inserting thefirst sensor into the bone structure comprises engaging a housing of thefirst sensor with a vertebra. In some instances, engaging the implantwith at least a portion of the bone structure comprises inserting aspinal implant. In some instances, the steps of inserting and securingthe first sensor comprise positioning the first sensor within a portionof a long bone. In some instances, engaging the implant with at least aportion of the bone structure comprises inserting an elongated implantinto an intramedullary canal of the long bone.

The foregoing outlines features of several embodiments so that thoseskilled in the art may better understand the aspects of the presentdisclosure. Those skilled in the art should appreciate that they mayreadily use the present disclosure as a basis for designing or modifyingother processes and structures for carrying out the same purposes and/orachieving the same advantages of the embodiments introduced herein.Those skilled in the art should also realize that such equivalentconstructions do not depart from the spirit and scope of the presentdisclosure, and that they may make various changes, substitutions andalterations herein without departing from the spirit and scope of thepresent disclosure. Further, while numerous embodiments have beendescribed it is fully contemplated that steps from various methods maybe combined and components from various devices and systems may becombined, even if not explicitly described herein.

1. A method of patient assessment and outcome modeling, comprising:obtaining patient characteristic information from a current patient;defining one or more desirable outcome factors based on thecharacteristic information of the current patient; weighting the one ormore desirable outcome factors; accessing at least one database havingmedical records of prior patients, the medical records including atleast prior patient characteristic information, prior patient treatmentplan, and prior patient outcome; comparing the weighted factors of thecurrent patient to the medical records of the prior patients to identifyone or more prior patient records relevant to the patientcharacteristics information of the current patient; retrieving at leasta portion of the relevant prior patient records, the portion includingat least the prior patient treatment plan and the prior patient outcome;and performing a simulation of at least one of the prior patienttreatment plans based on the current patient's characteristicinformation.
 2. The method of claim 1, further comprising identifying atleast two available treatment plans for the current patient and rankingthe at least two available treatment plans.
 3. The method of claim 2,further comprising performing a simulation of the at least two availabletreatment plans and wherein the ranking is at least partially based onthe simulations.
 4. The method of claim 3, wherein the ranking is atleast partially based on the success of the available treatment plansfor the one or more relevant prior patient records.
 5. The method ofclaim 4, wherein said prior treatments and said administered treatmentsinclude a spinal surgical procedure.
 6. The method of claim 5, furthercomprising: selecting one of the at least two available treatment plans;performing the selected treatment plan; comparing one or more actualoutcome factors to the one or more desirable outcome factors; updatingthe at least one database with a medical record of the current patient,including at least the selected treatment plan and the one or moreactual outcome factors.
 7. A method for identifying available treatmentoptions for a patient having an increased likelihood of success,comprising: obtaining a plurality of therapeutic factors from a currentpatient, said factors based at least partially on the current patient'sphysical characteristics, pathology, and desired therapeutic outcomes;weighting the therapeutic factors; accessing at least one databasehaving records of prior patient treatments, including prior patienttherapeutic factors, treatment plans, and treatment outcomes; comparingthe therapeutic factors of the current patient with the prior patienttherapeutic factors in the records of the database to identify priorpatients with similar therapeutic factors; retrieving from the databaseat least a portion of one or more records of prior patients with similartherapeutic factors; and identifying available treatment options for thecurrent patient based at least in part on the records of the priorpatients with similar therapeutic factors.
 8. The method of claim 7,wherein the available treatment options are identified based on thesuccess of the treatment options with prior patients with similartherapeutic factors.
 9. The method of claim 8, further comprisingsimulating the one or more available treatment options based on thecurrent patient's physical characteristics, pathology, and desiredtherapeutic outcomes.
 10. The method of claim 9, wherein a specifictreatment option is selected from the one or more available treatmentoptions at least partially based on the simulating of the one or moretreatment plans.
 11. The method of claim 10, wherein the specifictreatment option is selected at least partially based on the treatmentoutcomes of the prior patients with similar therapeutic factors.
 12. Themethod of claim 11, further comprising ranking the available treatmentoptions.
 13. The method of claim 12, wherein the ranking is at leastpartially based on simulating the available treatment options.
 14. Themethod of claim 13, wherein the ranking is at least partially based onthe prior patient outcomes.
 15. The method of claim 7, comprising:identifying at least one patient symptom; selecting at least one patientcategory associated with the at least one patient symptom; obtainingdata corresponding to the at least one patient category; providing theobtained data to a software application; analyzing the obtained datawith the software application; and providing a summary of the softwareapplication analysis for use in diagnosing the patient's medicalcondition and identifying available treatment options.
 16. The method ofclaim 15, wherein selecting the at least one patient category comprisesselecting a patient category from a predefined set of patientcategories.
 17. The method of claim 16, wherein each patient category ofthe predefined set of patient categories includes an associated datacollection set that defines a plurality of data items corresponding tothe patient category.
 18. The method of claim 17, wherein identifyingavailable treatment options for the current patient comprises comparingthe summary to a prior patient data set to identify previouslysuccessful treatment plans of prior patients in a similar patientcategory.
 19. A system for identifying available treatment options for acurrent patient having an increased likelihood of success, comprising:at least one local database having a plurality of records of prior localpatients, the records including patient characteristic information,treatment information, and outcome information; at least one remotedatabase having a plurality of records of prior remote patients, therecords including patient characteristic information, treatmentinformation, and outcome information; at least one processing systemoperatively connected to the local and remote databases, the at leastone processing system comprising a diagnostic module, a modeling module,and a treatment module; the diagnostic module being configured toreceive and weight current patient information, compare the currentpatient information to the plurality of records of in the local andremote databases, and retrieve records of prior patients with similarcharacteristic information from the local and remote databases, thetreatment module being configured to identify available treatmentoptions for the current patient based at least partially on the recordsretrieved from the local and remote databases by the diagnostic module,the modeling module being configured to simulate the available treatmentoptions for the current patient identified by the treatment module,wherein the simulation is at least partially based on the outcomeinformation from the records of prior patients retrieved from the localand remote databases.
 20. The system of claim 19, wherein the treatmentinformation stored in the local and remote databases includes medicalproducts used in the treatment.